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 .
Response to Arguments
The objections to the Drawings are withdrawn in view of the amendment filed on 8/18/2026.
The objections to the specification is withdrawn in view of the amendment filed on 8/18/2026.
The objections to the claims 27 and 35 are withdrawn in view of the amendment filed on 8/18/2026.
Applicant’s arguments in view of the claim amendments filed on 8/18/2026, with respect to the 35 U.S.C. §112 rejections have been fully considered and are persuasive. The 35 U.S.C. §112 rejections have been withdrawn.
Applicant’s arguments in view of the claim amendments filed on 8/18/2026, with respect to the 35 U.S.C. §101 rejections have been fully considered and are persuasive. The 35 U.S.C. §101 rejections have been withdrawn.
Applicant’s arguments in view of the claim amendments filed on 8/18/2026, with respect to the prior art rejection have been fully considered and are persuasive. The 35 U.S.C. §103 rejections have been withdrawn.
Applicant’s arguments in view of the claim amendments with respect to the prior art rejections have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further search and consideration of the amended claims, new grounds of rejection are made.
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 32 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 32 recites the limitation "the neural network device" in lines 1-2 ("the functional activity in the neural network device that is responsive to the input includes signal transmission along edges of the neural network device"). There is insufficient antecedent basis for this limitation in the claim. Claim 28, from which claim 32 depends, does not recite "a neural network device." As amended, claim 28 introduces both "a first neural network device" and "a second neural network device", so it is unclear which device "the neural network device" refers to. The two readings differ in scope. One limits the activity recorded and characterized in claim 28 (first device). The other limits activity in the reconstructed device (second device), which claim 28 does not measure. A person of ordinary skill in the art would not be reasonably apprised of the scope of the claim. For purposes of examination, "the neural network device" in claim 32 is interpreted under BRI as "the first neural network device" introduced in claim 28. Claim 28 recites "functional activity in the first neural network device that is responsive to the input," and claim 32 further limits "the functional activity ... that is responsive to the input.". Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 21-26 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Topological analysis of the connectome of digital reconstructions of neural microcircuits to Dlotko et al. (hereinafter Dlotko).
Per claim 21, Dlotko discloses A computer-implemented method (Dlotko: p. 1, Abstract…Dlotko performs computational topological analysis of simulated neuronal activity in a digitally reconstructed microcircuit and represents the resulting complexes in software data structures, which constitutes the computer-implemented method under BRI, "Functional topological analysis of simulated neuronal activity in the microcircuit revealed novel spatio-temporal metrics that provide an effective classification of functional responses to qualitatively different stimuli"; p. 10, §4.1…"We represent the directed flag complex of a directed graph by a reference-based data structure, using vectors to store the references to the simplices in the simplicial complex"), comprising:
receiving, at a neural network device, a first input (Dlotko: p. 2…Dlotko feeds a stimulus into a digitally reconstructed and simulated neocortical microcircuit through its thalamo-cortical connections; the simulated microcircuit is a software system of interconnected nodes mimicking biological neurons, which constitutes the neural network device, and the applied stimulus constitutes the first input under BRI, "Topological methods also enabled us to distinguish functional responses to different input patterns fed into the microcircuit through thalamo-cortical connections. We ran simulations of neural activity in one of the reconstructed microcircuits during one second, over the course of which a given stimulus was applied every 50 ms (Figure 3)"; p. 1…"The Blue Brain Project (BBP) has recently generated the first draft digital reconstruction and simulation of a microcircuit of neurons in the neocortex of a two-week-old rat (Figure 1A) [7]");
dividing functional activity in the neural network device that is responsive to the first input into first time bins (Dlotko: p. 7, §2…Dlotko divides the stimulus-evoked activity of the microcircuit into 5 ms time bins following stimulation, which constitutes the dividing … into first time bins under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms");
recording, for each of the first time bins, a first measure of the functional activity in the neural network device during that first time bin that is responsive to the first input (Dlotko: p. 7, §2…Dlotko records, for each 5 ms bin n, a transmission-response matrix A(n) whose entries mark structural connections whose presynaptic spike in that bin is followed by postsynaptic firing, which constitutes the first measure of the functional activity recorded for each first time bin under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 7, §2…"We call the matrices A(n) transmission-response matrices, as it is reasonable to assume that the spiking of neuron k is influenced by the spiking of neuron j under conditions (1)–(3) above");
characterizing one or more parameters of the recorded first measure of the functional activity using topological methods (Dlotko: p. 7, §2…Dlotko computes, for the transmission-response graph of each time bin, Betti numbers, simplex counts and the Euler characteristic, which constitutes the characterizing one or more parameters … using topological methods under BRI, "In Figure 4C we provide plots of the time series of the average zeroth, first, and second Betti numbers, of the average numbers of 1- and 2-simplices, and of the average Euler characteristic for 20 trials of each stimulus");
receiving, at the neural network device, a second input (Dlotko: p. 6, §2…Dlotko presents a second, different stimulus (the Circle stimulus, as against the Point stimulus) to the same microcircuit in its own set of 20 trials, which constitutes the second input under BRI, "The size of the point shape was chosen such that the average firing rate of the neurons was essentially the same as for the circle shape, and in both cases the fibers were activated regularly and synchronously with a frequency of 20 Hz for one second, similar to the whisker deflection approximation in [7, Figure 17A]. We performed 20 trials of each stimulus (Figure 3)");
dividing functional activity in the neural network device that is responsive to the second input into second time bins (Dlotko: p. 7, §2…Dlotko bins the activity of every trial into 5 ms bins after the initial stimulation, and runs 20 trials of each stimulus, so the activity evoked by the second stimulus is divided into its own second time bins, which constitutes the dividing … into second time bins under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 6, §2…"The size of the point shape was chosen such that the average firing rate of the neurons was essentially the same as for the circle shape, and in both cases the fibers were activated regularly and synchronously with a frequency of 20 Hz for one second, similar to the whisker deflection approximation in [7, Figure 17A]. We performed 20 trials of each stimulus (Figure 3)");
recording, for each of the second time bins, a second measure of the functional activity in the neural network device during that second time bin that is responsive to the second input (Dlotko: p. 7, §2…Dlotko records the matrix A(n) for each bin of each trial, including the trials of the second stimulus, which constitutes the second measure recorded for each second time bin under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 6, §2…"The size of the point shape was chosen such that the average firing rate of the neurons was essentially the same as for the circle shape, and in both cases the fibers were activated regularly and synchronously with a frequency of 20 Hz for one second, similar to the whisker deflection approximation in [7, Figure 17A]. We performed 20 trials of each stimulus (Figure 3)");
characterizing one or more parameters of the recorded second measure of the functional activity using topological methods (Dlotko: p. 8, Fig. 4(C)…Dlotko computes the same topological metrics for the trials of each stimulus and plots them separately for the Circle and Point stimuli, which constitutes the characterizing … of the recorded second measure … using topological methods under BRI, "(C) Time series plots of the average value of the metrics 1D (number of 1-simplices), 2D (number of 2-simplices), β0 (the zeroth Betti number, i.e., the number of connected components), β1 (the first Betti number), β2 (the second Betti number), and EC (the Euler characteristic) for the Circle and Point stimuli"; p. 7, §2…"In Figure 4C we provide plots of the time series of the average zeroth, first, and second Betti numbers, of the average numbers of 1- and 2-simplices, and of the average Euler characteristic for 20 trials of each stimulus"); and
distinguishing the functional activity of the neural network device that is responsive to the first input from the functional activity of the neural network device that is responsive to the second input based on the characterized topological parameters (Dlotko: p. 7, §2…Dlotko classifies each trial as Point or Circle from the per-bin topological metrics with a Gaussian Bayes classifier and reports that the simplex counts and Betti numbers separate the two stimuli, which constitutes the distinguishing … based on the characterized topological parameters under BRI, "On the other hand, in the very first time bin, immediately after the initial stimulation, the 1- and 2-dimensional simplex counts and β1 and β2 all classify very well"; p. 8, §3…"We established moreover that topological methods effectively distinguish functional responses to distinct thalamic stimuli, introducing a new measure of the spatio-temporal activity responses generated by neural tissue").
Per claim 22, Dlotko discloses claim 21, further disclosing wherein the topological methods comprise determining associated directed flag complexes (Dlotko: p. 3, Fig. 1(C)…Dlotko defines its simplices as the simplices of the directed flag complex associated with a directed graph, one n-simplex per directed (n+1)-clique, and counts those simplices and computes Betti numbers for the directed transmission-response graph of each time bin, so its topological methods determine the associated directed flag complexes, which constitutes the determining associated directed flag complexes under BRI, "(C) An example of a directed graph and its associated flag complex, in which there is one n-simplex for every directed (n+1)-clique in the graph"; p. 7, §2…"In Figure 4C we provide plots of the time series of the average zeroth, first, and second Betti numbers, of the average numbers of 1- and 2-simplices, and of the average Euler characteristic for 20 trials of each stimulus").
Per claim 23, Dlotko discloses claim 21, further disclosing wherein the functional activity in the neural network device that is responsive to the first and second inputs includes signal transmission along edges of the neural network device (Dlotko: p. 8, Fig. 4(A)…an edge enters Dlotko's transmission-response graph only when a spike is transmitted along a physical connection and elicits a postsynaptic spike, so the recorded functional activity is signal transmission along edges of the microcircuit, which constitutes the signal transmission along edges under BRI, "(A) Schematic representation of the transmission-response paradigm: there will be an edge from j to k in the graph associated to particular time bin if and only if there is a physical connection from neuron j to neuron k, neuron j fires in the time bin, and neuron k fires at most 7.5 ms after the firing of neuron j").
Per claim 24, Dlotko discloses claim 21, further disclosing wherein a duration of each of the first time bins and each of the second time bins is constant (Dlotko: p. 7, §2…every bin in Dlotko spans the same 5 ms for both stimuli, which constitutes the constant duration of each of the first time bins and each of the second time bins limitation under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms").
Per claim 25, Dlotko discloses claim 21, further disclosing wherein the first measure of the functional activity and the second measure of the functional activity are functional connectivity matrices (Dlotko: p. 7, §2…Dlotko expressly calls each per-bin matrix A(n) a functional connectivity matrix, which constitutes the functional connectivity matrices under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 7, §2…"We call the matrices A(n) transmission-response matrices, as it is reasonable to assume that the spiking of neuron k is influenced by the spiking of neuron j under conditions (1)–(3) above").
Per claim 26, Dlotko discloses claim 21, further disclosing wherein the first input and the second input are known inputs (Dlotko: p. 7, §2…Dlotko's Point and Circle stimuli are predetermined by the experimenter and their trial labels are known, since the labeled trials are split into training and testing sets for the classifier, which constitutes the known inputs under BRI, "The goal of the Point vs. Circle test was to determine whether topological metrics, such as simplex counts, Betti numbers and Euler characteristic, could classify correctly two groups of stimuli of a similar nature and whether these metrics contain more information than the mean firing rate"; p. 11, §4.4…"We randomly split the data into training and testing sets").
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 21-24 and 26 are rejected under 35 USC 103 as being unpatentable over The topology of the directed clique complex as a network invariant to Masulli et al. (previously cited, hereinafter Masulli) in view of Cell groups reveal structure of stimulus space to Curto et al. (previously cited, hereinafter Curto).
Per claim 21, Masulli discloses A computer-implemented method (Masulli: p. 1, Abstract…Masulli simulates a Boolean recurrent artificial neural network on a computer and computes topological invariants of its directed clique complex, which constitutes the computer-implemented method under BRI, "We investigate how the evolution of a Boolean recurrent artificial neural network is influenced by its topology in a dynamics involving pruning and strengthening of the connections, and to show that the topological features of the directed clique complex influence the dynamical evolution of the network"; p. 9…"The artificial recurrent neural networks consist of a finite number of Boolean neurons organized in layers with a convergent/divergent connection structure (Abeles, 1991)"), comprising:
receiving, at a neural network device, a first input (Masulli: p. 9…Masulli's simulated artificial recurrent network of interconnected Boolean neurons constitutes the neural network device, and the periodic activation of its input layer constitutes the first input under BRI, "The first layer is the input layer and all its 10 neurons get activated at the same time at a fixed frequency of 0.1, i.e. every 10 time steps of the history"; p. 9…"The artificial recurrent neural networks consist of a finite number of Boolean neurons organized in layers with a convergent/divergent connection structure (Abeles, 1991)");
dividing functional activity in the neural network device that is responsive to the first input into first time bins (Masulli: p. 3…Masulli evaluates the network's input-driven activity separately at each discrete time step, which constitutes the dividing … into first time bins under BRI, "We have computed the Euler characteristic and its variation during the evolution of such networks, both for the entirety of the nodes in the network and for the sub-network induced by the nodes that are active at each time step in order to detect how the structure changes as the network evolves");
recording, for each of the first time bins, a first measure of the functional activity in the neural network device during that first time bin that is responsive to the first input (Masulli: p. 9…Masulli records, at each time step, the active/inactive state of every neuron and forms the sub-network induced by the active neurons, which constitutes the first measure of the functional activity recorded for each first time bin under BRI, "The state Si(t) of a neuron i take values 0 (inactive) or 1 (active) and all Boolean neurons are set inactive at the beginning of the simulation"; p. 3…"We have computed the Euler characteristic and its variation during the evolution of such networks, both for the entirety of the nodes in the network and for the sub-network induced by the nodes that are active at each time step in order to detect how the structure changes as the network evolves");
characterizing one or more parameters of the recorded first measure of the functional activity using topological methods (Masulli: p. 3…Masulli computes the Euler characteristic of the directed clique complex of the active sub-network at each time step, which constitutes the characterizing one or more parameters … using topological methods under BRI, "We have computed the Euler characteristic and its variation during the evolution of such networks, both for the entirety of the nodes in the network and for the sub-network induced by the nodes that are active at each time step in order to detect how the structure changes as the network evolves"; p. 2…"In the current study we introduce a mathematical object, called directed clique complex, encoding the link structure of networks in which the edges (or links) have a given orientation. This object is a simplicial complex that can be studied with the techniques of algebraic topology to obtain invariants such as the Euler characteristic and the Betti numbers").
Masulli does not expressly disclose, but Curto does teach:
receiving, at the neural network device, a second input (Curto: p. 3…Curto drives a simulated neuronal ensemble with activity generated in each of five different environments, so the activity driven by a second environment constitutes the second input under BRI, "In each of 300 trials, each of the five environments was covered by 70 single-peaked place fields with varying radii (0.1–0.15 L) and randomly-chosen centers (Figure 2A). Place cell firing was generated according to a simple model (see Methods)");
dividing functional activity in the neural network device that is responsive to the second input into second time bins (Curto: p. 9, Methods…Curto bins the population spike trains evoked in each environment into 2-theta-cycle time bins, which constitutes the dividing … into second time bins under BRI, "To determine the full set of cell groups that become activated as the animal traverses the environment, we first bin population spike trains into 2-theta-cycle time bins. A certain subset of cells fires in each time bin, and we use these subsets to determine the cell groups");
recording, for each of the second time bins, a second measure of the functional activity in the neural network device during that second time bin that is responsive to the second input (Curto: p. 9, Methods…Curto records, for each time bin, the cell group of neurons firing significantly above baseline, which constitutes the second measure recorded for each second time bin under BRI, "Each population vector thus yielded a cell group, consisting of all cells firing significantly above baseline in a particular time bin");
characterizing one or more parameters of the recorded second measure of the functional activity using topological methods (Curto: p. 3…Curto computes the homology groups of the simplicial complex built from the recorded cell groups for each trial, which constitutes the characterizing … of the recorded second measure … using topological methods under BRI, "For each trial, the first five homology groups (H0,…,H4) were computed"); and
distinguishing the functional activity of the neural network device that is responsive to the first input from the functional activity of the neural network device that is responsive to the second input based on the characterized topological parameters (Curto: p. 10, Methods…Curto identifies which environment drove the activity from its Betti numbers, the first Betti number differing between environments, which constitutes the distinguishing … based on the characterized topological parameters under BRI, "The 1st Betti number β1, on the other hand, is different for each of the five environments, matching the number of holes in each"; p. 3…"Although the correct environment could be identified using only the first homology group H1, we required the other homology groups to also match in order to ensure consistency of the overall topology").
Masulli and Curto are analogous art because they are from the same field of endeavor, specifically the algebraic-topological analysis of the activity of simulated neuronal networks. They address the same problem-solving area of extracting simplicial-complex invariants, such as Betti numbers and the Euler characteristic, from time-stepped or time-binned neural activity to characterize what that activity represents.
Before the effective filing date of the claimed invention, it would have been obvious to a PHOSITA to present Masulli's simulated network with a second input pattern, bin and record its activity in the same manner, compute the same topological invariants, and compare the invariants obtained for the two inputs to tell the inputs apart as taught by Curto, as claimed.
The suggestion/motivation for doing so would have been provided by Masulli itself, which teaches that its topological invariants are a tool for assessing the functional features of a network, "The application of the Euler characteristic presented here can be extended to any directed network and provides a new method for the assessment of specific functional features associated with the network topology" (Masulli: p. 1, Abstract), and by Curto, which teaches that topological invariants computed from binned activity identify which stimulus space drove that activity, "Although the correct environment could be identified using only the first homology group H1, we required the other homology groups to also match in order to ensure consistency of the overall topology" (Curto: p. 3). A PHOSITA would have reasonably expected success because Curto reports that the topological identification remains correct even when the binned activity is noisy, "Remarkably, the percentage of correct trials remained very high for noise levels up to 5%; even with 10% of each cell’s spikes occurring outside the corresponding place field, more than half of all trials continued to be correct (Figure 2B)" (Curto: p. 3). Furthermore, this is the use of a known technique to improve similar methods in the same way, the rationale of MPEP § 2143(I)(C).
Per claim 22, Masulli combined with Curto discloses claim 21. Masulli further teaches wherein the topological methods comprise determining associated directed flag complexes (Masulli: p. 6…Masulli defines each simplex of its directed clique complex as a fully connected directed sub-network with ordered nodes, one source and one sink, the same construction as a directed flag complex, which constitutes the determining associated directed flag complexes under BRI, "By definition, a directed clique (or a simplex in our complex) is a fully-connected directed sub-network (Figure 3) this means that the nodes are ordered and there is one source and one sink in the sub-network, and the presence of the directed clique in the network means that the former is connected to the latter in all the possible ways within the sub-network").
Per claim 23, Masulli combined with Curto discloses claim 21. Masulli further teaches wherein the functional activity in the neural network device that is responsive to the first and second inputs includes signal transmission along edges of the neural network device (Masulli: p. 9…each Masulli neuron's activation is computed from the signals of the neurons projecting to it, weighted by the directed connections, so the recorded activity is signal transmission along the network's edges, which constitutes the signal transmission along edges under BRI, "At each time step, the value Vi(t) of the activation variable of the ith neuron is calculated such that Vi(t+1) = Σj Sj(t)wji(t), where wji(t) are the weights of the directed connections from any jth neuron projecting to neuron i").
Per claim 24, Masulli combined with Curto discloses claim 21. Masulli further teaches wherein a duration of each of the first time bins and each of the second time bins is constant (Masulli: p. 9…Masulli updates all neurons synchronously in uniform discrete time steps, so every time bin has the same duration, which constitutes the constant duration under BRI, "The weights of all the neurons are computed synchronously at each time step").
Per claim 26, Masulli combined with Curto discloses claim 21. Curto further teaches wherein the first input and the second input are known inputs (Curto: p. 3…Curto's environments are constructed in advance by the experimenter, and each trial is scored against the known topology of its environment, so both the first and the second inputs are known, which constitutes the known inputs under BRI, "In each of 300 trials, each of the five environments was covered by 70 single-peaked place fields with varying radii (0.1–0.15 L) and randomly-chosen centers (Figure 2A). Place cell firing was generated according to a simple model (see Methods)"; p. 3…"Although the correct environment could be identified using only the first homology group H1, we required the other homology groups to also match in order to ensure consistency of the overall topology"). The rationale to combine Curto with Masulli is the same as the parent claim.
Claim 25 is rejected under 35 USC 103 as being unpatentable over Masulli in view of Curto, as applied to claim 21 above, and further in view of Clique topology reveals intrinsic geometric structure in neural correlations to Giusti et al. (previously cited, hereinafter Giusti).
Per claim 25, Masulli combined with Curto discloses claim 21. Masulli combined with Curto does not expressly disclose, but Giusti does teach:
wherein the first measure of the functional activity and the second measure of the functional activity are functional connectivity matrices (Giusti: p. 2…Giusti represents neural activity as a matrix of pairwise correlations between neurons computed over a chosen correlation timescale, which constitutes the functional connectivity matrices under BRI when applied to the per-time-bin activity of the combination, "Neural activity and connectivity data are often presented in the form of a matrix whose entries, Cij, indicate the strength of correlation or connectivity between pairs of neurons, cell types, or imaging voxels"; p. 7…"We also repeated the analyses for different choices of the correlation timescale, τmax, ranging from 10 ms to 2 s, and observed similar results (Figure 3d)").
Masulli, Curto and Giusti are analogous art because they are from the same field of endeavor, specifically the algebraic-topological analysis of neuronal activity. They address the same problem-solving area of computing clique-complex and simplicial-complex invariants of recorded neural activity.
Before the effective filing date of the claimed invention, it would have been obvious to a PHOSITA to record the per-time-step functional activity of the Masulli and Curto combination as a matrix of pairwise activity relationships between neurons, as taught by Giusti, so that each first measure and each second measure is a functional connectivity matrix.
The suggestion/motivation for doing so would have been provided by Masulli itself, which points to Giusti's matrix-based topological invariant as a complementary measure for neural networks, "A distinct topological invariant defined for non-directed networks, referred to the Betti curves, was recently proposed by Giusti et al (2015) following the idea of filtering the network by the weight of connections" (Masulli: p. 5), and by Giusti, which teaches that clique topology is well suited to analyzing such matrices of neural data, "We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown" (Giusti: p. 1, Abstract). Furthermore, this is the use of a known technique to improve similar methods in the same way, the rationale of MPEP § 2143(I)(C).
Claim 27 is rejected under 35 USC 103 as being unpatentable over Dlotko in view of A topological paradigm for hippocampal spatial map formation using persistent homology to Dabaghian et al. (previously cited, hereinafter Dabaghian).
Per claim 27, Dlotko discloses claim 21. Dlotko does not expressly disclose, but Dabaghian does teach:
wherein the method further comprises determining that the neural network device is functioning properly or trained based on the distinguishing of the functional activity of the neural network device that is responsive to the first input from the functional activity of the neural network device that is responsive to the second input (Dabaghian: p. 4…Dabaghian determines that a simulated neuronal ensemble has learned its environment at the time when the Betti numbers computed from its co-activity stabilize at the correct values for that environment, each of its test environments having its own correct number of loops, which constitutes the determining that the neural network device is … trained based on the distinguishing under BRI, "The time required for the correct number of bars (cycles) to appear in every dimension is, by design, the time required to extract the correct topological signature of the environment, which can thus be interpreted as the minimal time T̄min required for the rat to learn the environment"; p. 3…"Eventually, after a certain minimal time T̄min, the structure of T(t) should saturate, and its topological characteristics should stabilize and produce the correct topological indices, which would indicate the completeness of the topological information"; p. 5…"We asked whether, and for which ensembles, the place cell spiking signals would be able to produce a temporal simplicial complex with the correct number of topological loops (Betti numbers; see Methods) in every dimension—the connectivity of the space (0D loops) and the correct number of 1D loops").
Dlotko and Dabaghian are analogous art because they are from the same field of endeavor, specifically the topological analysis of simulated neuronal activity. They address the same problem-solving area of computing simplicial-complex invariants from co-activity of neurons within short time windows to characterize how a network represents its inputs.
Before the effective filing date of the claimed invention, it would have been obvious to a PHOSITA to use the point at which Dlotko's topological metrics reliably distinguish the responses to the two stimuli as the indication, taught by Dabaghian, that the network has learned, that is, been trained on, its inputs.
The suggestion/motivation for doing so would have been provided by Dlotko itself, which teaches that the topology of its digital reconstructions can expose learning-related structure, "Age-dependent changes in such digital reconstructions may help reveal even more complex topological structures with development, and changes introduced by synaptic plasticity may reveal structures associated with learning and memory" (Dlotko: p. 10, §3), and by Dabaghian, which teaches that the stabilization of topological indices computed from neural co-activity marks the completion of learning, "Eventually, after a certain minimal time T̄min, the structure of T(t) should saturate, and its topological characteristics should stabilize and produce the correct topological indices, which would indicate the completeness of the topological information" (Dabaghian: p. 3). Furthermore, this is the use of a known technique to improve similar methods in the same way, the rationale of MPEP § 2143(I)(C).
Claims 28-35 are rejected under 35 USC 103 as being unpatentable over Dlotko in view of Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations to van Albada et al. (hereinafter van Albada), and further in view of Synchronization from second order network connectivity statistics to Zhao et al. (hereinafter Zhao).
Per claim 28, Dlotko discloses A computer-implemented method (Dlotko: p. 1, Abstract…Dlotko performs computational topological analysis of simulated neuronal activity in a digitally reconstructed microcircuit, which constitutes the computer-implemented method under BRI, "Functional topological analysis of simulated neuronal activity in the microcircuit revealed novel spatio-temporal metrics that provide an effective classification of functional responses to qualitatively different stimuli"; p. 10, §4.1…"We represent the directed flag complex of a directed graph by a reference-based data structure, using vectors to store the references to the simplices in the simplicial complex"), comprising:
receiving, at a first neural network device, an input (Dlotko: p. 2…Dlotko feeds a stimulus into a digitally reconstructed and simulated neocortical microcircuit, which constitutes the first neural network device, and the stimulus constitutes the input under BRI, "Topological methods also enabled us to distinguish functional responses to different input patterns fed into the microcircuit through thalamo-cortical connections. We ran simulations of neural activity in one of the reconstructed microcircuits during one second, over the course of which a given stimulus was applied every 50 ms (Figure 3)"; p. 1…"The Blue Brain Project (BBP) has recently generated the first draft digital reconstruction and simulation of a microcircuit of neurons in the neocortex of a two-week-old rat (Figure 1A) [7]");
dividing functional activity in the first neural network device that is responsive to the input into time bins (Dlotko: p. 7, §2…Dlotko divides the stimulus-evoked activity into 5 ms time bins, which constitutes the dividing … into time bins under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms");
recording, for each of the time bins, a measure of the functional activity in the first neural network device during that time bin that is responsive to the input (Dlotko: p. 7, §2…Dlotko records a transmission-response matrix A(n) for each time bin, which constitutes the measure of the functional activity recorded for each time bin under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 7, §2…"We call the matrices A(n) transmission-response matrices, as it is reasonable to assume that the spiking of neuron k is influenced by the spiking of neuron j under conditions (1)–(3) above");
characterizing one or more parameters of the recorded measure of the functional activity using topological methods (Dlotko: p. 7, §2…Dlotko computes Betti numbers, simplex counts and the Euler characteristic of each per-bin transmission-response graph, which constitutes the characterizing one or more parameters … using topological methods under BRI, "In Figure 4C we provide plots of the time series of the average zeroth, first, and second Betti numbers, of the average numbers of 1- and 2-simplices, and of the average Euler characteristic for 20 trials of each stimulus"); and
Dlotko does not expressly disclose, but van Albada does teach:
reconstructing at least some of functioning of the first neural network device in a second neural network device … (van Albada: p. 3…van Albada constructs a second, downscaled network whose synaptic weights are set so that statistics of quantities of interest measured in the full first network are matched in the second network, so the second network behaves like a subsample of the first, which constitutes the reconstructing at least some of functioning of the first neural network device in a second neural network device under BRI, "One intuitive way of scaling is to ensure that the statistics of particular quantities of interest in the downscaled network match those of a subsample of the same size from the full network (Fig 1D)"; p. 3, Fig. 1(D)…"When average single-neuron and pairwise properties are preserved, the downscaled network of size N behaves to second order like a subsample of the full network of size N0").
Dlotko combined with van Albada does not expressly disclose, but Zhao does teach:
… using the one or more characterizations to provide a target level of structuring or ordering of information flow within the second neural network device (Zhao: p. 7, Results…Zhao generates networks whose connectivity-motif statistics (reciprocal, convergent, divergent and chain connections) are set to prescribed values and shows that those values predictably determine the network's synchrony, so a characterization of motif structure is used to provide a target level of ordering of activity in the generated network, which constitutes the using the one or more characterizations to provide a target level of structuring or ordering of information flow under BRI, "We generate networks using the framework of SONETs which allows us to systematically vary second order connectivity statistics, which are the frequency of reciprocal, convergent, divergent, and chain connections shown in Figure 1 (see Materials and Methods)"; p. 1, Abstract…"The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections").
Dlotko, van Albada and Zhao are analogous art because they are from the same field of endeavor, specifically the computational modeling and analysis of spiking network models of cortical circuits. They address the same problem-solving area of characterizing how the higher-order structure of a network's connectivity and activity shapes its function beyond mean firing rates.
Before the effective filing date of the claimed invention, it would have been obvious to a PHOSITA to reconstruct Dlotko's simulated microcircuit in a second network following van Albada, and to generate that second network with prescribed motif statistics following Zhao, taking as the prescribed values the motif and simplex characterizations of functional activity that Dlotko computes, so that the second network reproduces a target level of structuring or ordering of information flow, as claimed.
The suggestion/motivation for doing so would have been provided by van Albada itself, which teaches that reduced network models should preserve statistics beyond firing rates, "While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained" (van Albada: p. 1, Abstract), by Zhao, which teaches generating realistic network structure from key second-order connectivity statistics, "We connect the synchronizability analysis directly to network structure using the recently developed framework of second order networks (SONETs), which is an approach to characterize and generate realistic network structure using key second order statistics of connectivity (Zhao et al., in preparation)" (Zhao: p. 1, Introduction), and by Dlotko, which teaches that its topological metrics capture the stimulus-specific organization of activity better than firing rates, "In all of these cases, the topological metrics far outperform the metric based on firing rate" (Dlotko: p. 8, §2). A PHOSITA would have reasonably expected success because Zhao reports that the dependence of synchrony on the prescribed motif statistics holds across models and network types, "These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks" (Zhao: p. 1, Abstract). Furthermore, this is some teaching, suggestion, or motivation in the prior art that would have led a PHOSITA to combine the prior-art teachings to arrive at the claimed invention, the rationale of MPEP § 2143(I)(G).
Per claim 29, Dlotko combined with van Albada combined with Zhao discloses claim 28. van Albada further teaches wherein reconstructing at least some of the functioning of the first neural network device in the second neural network device comprises copying or mimicking at least some of the function of the first neural network device in the second neural network device (van Albada: p. 3, Fig. 1(D)…van Albada's downscaled network reproduces the full network's single-neuron and pairwise statistics and behaves like a subsample of it, which constitutes the copying or mimicking at least some of the function under BRI, "When average single-neuron and pairwise properties are preserved, the downscaled network of size N behaves to second order like a subsample of the full network of size N0"). The rationale to combine van Albada with Dlotko is the same as the parent claim.
Per claim 30, Dlotko combined with van Albada combined with Zhao discloses claim 28. van Albada further teaches wherein the functioning of the first neural network device is reconstructed in the second neural network device without training the second neural network device (van Albada: p. 21, Discussion…van Albada reaches the target statistics by analytically rescaling synaptic strengths and the external drive rather than by any training procedure, which constitutes the without training the second neural network device under BRI, "The most straightforward way of simultaneously preserving mean activities and pairwise averaged correlations is to change the synaptic strengths in inverse proportion to the in-degrees (J ∝ 1/K), and to adjust the variance of the external drive to make up for the change in variance of inputs from within the network"). The rationale to combine van Albada with Dlotko is the same as the parent claim.
Per claim 31, Dlotko combined with van Albada combined with Zhao discloses claim 28. Dlotko further teaches wherein the topological methods comprise determining associated directed flag complexes (Dlotko: p. 3, Fig. 1(C)…Dlotko's simplices are those of the directed flag complex of each directed graph, and Dlotko counts them and computes Betti numbers for each per-bin transmission-response graph, which constitutes the determining associated directed flag complexes under BRI, "(C) An example of a directed graph and its associated flag complex, in which there is one n-simplex for every directed (n+1)-clique in the graph"; p. 7, §2…"In Figure 4C we provide plots of the time series of the average zeroth, first, and second Betti numbers, of the average numbers of 1- and 2-simplices, and of the average Euler characteristic for 20 trials of each stimulus").
Per claim 32, Dlotko combined with van Albada combined with Zhao discloses claim 28. Dlotko further teaches wherein the functional activity in the neural network device that is responsive to the input includes signal transmission along edges of the neural network device (Dlotko: p. 8, Fig. 4(A)…Dlotko's functional activity is spike transmission along physical connections that elicits postsynaptic firing, which constitutes the signal transmission along edges under BRI, "(A) Schematic representation of the transmission-response paradigm: there will be an edge from j to k in the graph associated to particular time bin if and only if there is a physical connection from neuron j to neuron k, neuron j fires in the time bin, and neuron k fires at most 7.5 ms after the firing of neuron j").
Per claim 33, Dlotko combined with van Albada combined with Zhao discloses claim 28. Dlotko further teaches wherein the duration of the time bins is constant (Dlotko: p. 7, §2…every Dlotko time bin spans 5 ms, which constitutes the constant duration under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms").
Per claim 34, Dlotko combined with van Albada combined with Zhao discloses claim 28. Dlotko further teaches wherein the measure of the functional activity is a functional connectivity matrix (Dlotko: p. 7, §2…Dlotko calls each per-bin matrix A(n) a functional connectivity matrix, which constitutes the functional connectivity matrix under BRI, "After a systematic analysis to determine the appropriate time bin size and conditions for probable spike transmission from one neuron to another (SI, Supplementary Methods, SM1.4), we divided the activity of the microcircuit into 5 ms time bins for 1 second after the initial stimulation and recorded for each 0 ≤ n < 200 a functional connectivity matrix A(n) for the times between 5n ms and 5(n + 1) ms"; p. 7, §2…"We call the matrices A(n) transmission-response matrices, as it is reasonable to assume that the spiking of neuron k is influenced by the spiking of neuron j under conditions (1)–(3) above").
Per claim 35, Dlotko combined with van Albada combined with Zhao discloses claim 28. van Albada further teaches wherein the method further comprises determining that the second neural network device is functioning properly or trained based on a functional activity of the second neural network device that is responsive to the input (van Albada: p. 14, Fig. 5…van Albada simulates the downscaled second network driven by its external input and confirms from that activity that the target correlations are preserved, which constitutes the determining that the second neural network device is functioning properly based on a functional activity of the second neural network device under BRI, "Scaling synaptic strengths as J ∝ 1/K can preserve correlations in networks with widely different firing rates. Results of simulations of a LIF network consisting of one excitatory and one inhibitory population (Table 2)"; p. 3…"One intuitive way of scaling is to ensure that the statistics of particular quantities of interest in the downscaled network match those of a subsample of the same size from the full network (Fig 1D)"). The rationale to combine van Albada with Dlotko is the same as the parent claim.
Claim 36 is rejected under 35 USC 103 as being unpatentable over Dlotko in view of van Albada and Zhao, as applied to claim 29 above, and further in view of Cognitive computing programming paradigm: a corelet language for composing networks of neurosynaptic cores to Amir et al. (hereinafter Amir).
Per claim 36, Dlotko combined with van Albada combined with Zhao discloses claim 29. Dlotko combined with van Albada combined with Zhao does not expressly disclose, but Amir does teach:
wherein reconstructing at least some of the functioning of the first neural network device comprises assembling a plurality of functional subnetworks and adding structural links between the functional subnetworks to achieve a desired processing result (Amir: Fig. 3…Amir builds a larger network by composing self-contained sub-networks of neurosynaptic cores (sub-corelets), each implementing a function, and interconnecting their output and input connectors, which constitutes the assembling a plurality of functional subnetworks and adding structural links between the functional subnetworks to achieve a desired processing result limitation under BRI, "(d) Create connections between sub-corelets by interconnecting a set of output connector pins to a set of input connector pins"; Fig. 3…"(f) The composed corelet encapsulates the sub-corelets and the internal connectivity between sub-corelets, while exposing the new input and output connectors").
Dlotko, van Albada, Zhao and Amir are analogous art because they are from the same field of endeavor, specifically the construction and analysis of networks of spiking neurons. They address the same problem-solving area of building a network whose structure yields a desired activity or function.
Before the effective filing date of the claimed invention, it would have been obvious to a PHOSITA to carry out the reconstruction of the Dlotko, van Albada and Zhao combination by composing reusable functional sub-networks and interconnecting them, as taught by Amir.
The suggestion/motivation for doing so would have been provided by Amir itself, which teaches that composing sub-networks from a library of reusable components permits construction of complex network functions, "Therefore, as our main contribution, we develop a new programming paradigm that permits construction of complex cognitive algorithms and applications while being efficient for TrueNorth and effective for programmer productivity" (Amir: Abstract), and "(c) a Corelet Library that acts as an ever-growing repository of reusable corelets from which programmers compose new corelets" (Amir: Abstract). Furthermore, this is the use of a known technique to improve similar methods in the same way, the rationale of MPEP § 2143(I)(C).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALAN CHEN/ Primary Examiner, Art Unit 2125
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