Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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.
Claims 21-36 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 21, 32 recite the limitation “topographic mapping” in lines 1 and 6, respectively. There is insufficient antecedent basis for this limitation in the claim. There is no mention of “topographic mapping” or even “topographic” in the Specification. In Paragraph 0062 of the Specification, there is a mention of mapping the relationship between the transmembrane potential at the soma with activation of a synapse and the synaptic input current, but it is not clear how this relates to “topographic mapping”. For examination purposes the examiner will treat “topographic mapping” as a mapping that is based on position or location of the neurons. The examiner suggests defining “topographic mapping”.
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 21-40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 21-31, 38-40 are method claims. Claims 32-37 are machine/system/product claims. Therefore, claims 21-40 are directed to either a process, machine, manufacture or composition of matter.
With respect to claim 21:
Step 2A – Prong 1:
A method for integrating topographic mappings into a neural network model of a biological brain, the method comprising: … wherein the neural network model comprises a plurality of interconnected neurons; (mental process – a person can recognize that the neural network model comprises a plurality of interconnected neurons.)
estimating parameters of numerical filters that filter inputs between respective neurons in the neural network model, wherein the parameters of the assigned numerical filters represent positions and functional roles of the neurons in the biological brain; (mental process – a person can manually estimate parameters of numerical filters that filter inputs between respective neurons in the neural network model with the assistance of a pen/paper.)
and assigning the estimated parameters to the numerical filters in the neural network model. (mental process – a person can manually assign the estimated parameters to the numerical filters in the neural network model with the assistance of a pen/paper.)
Step 2A – Prong 2: This judicial exception is not integrated into a practical application.
… providing the neural network model, … (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: High level recitation of providing/using a neural network model).
Step 2B: The claim does not include additional elements considered individually and in combination that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 22:
Step 2A – Prong 1:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises estimating, for each of one or more individual neurons in the neural network model and using direct filter extraction, parameters of the numerical filters from a modelled behavior of the individual neuron in a morphologically-detailed model of the biological brain. (mental process – a person can recognize that estimating the parameters of the numerical filters comprises estimating, for each of one or more individual neurons in the neural network model and using direct filter extraction, parameters of the numerical filters from a modelled behavior of the individual neuron in a morphologically-detailed model of the biological brain.)
With respect to claim 23:
Step 2A – Prong 1:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises estimating, for each of one or more groups of neurons in the neural network model and using implicit filter extraction, parameters of the numerical filters from a modelled behavior of the group of neurons in a morphologically-detailed model of the biological brain. (mental process – a person can recognize that estimating the parameters of the numerical filters comprises estimating, for each of one or more groups of neurons in the neural network model and using implicit filter extraction, parameters of the numerical filters from a modelled behavior of the group of neurons in a morphologically-detailed model of the biological brain.)
With respect to claim 24:
Step 2A – Prong 1:
The method of claim 21, wherein the parameters of the assigned numerical filters indicate whether corresponding synapses are inhibitory or excitatory. (mental process – a person can recognize that parameters of the assigned numerical filters indicate whether corresponding synapses are inhibitory or excitatory.)
With respect to claim 25:
Step 2A – Prong 1:
The method of claim 21, wherein the parameters of the assigned numerical filters indicate whether corresponding synapses are located on an apical or basal dendrite. (mental process – a person can recognize that parameters of the assigned numerical filters indicate whether corresponding synapses are located on an apical or basal dendrite.)
With respect to claim 26:
Step 2A – Prong 1:
The method of claim 21, wherein the parameters of the assigned numerical filters represent a distance between corresponding synapses and somas. (mental process – a person can recognize that parameters of the assigned numerical filters represent a distance between corresponding synapses and somas.)
With respect to claim 27:
Step 2A – Prong 1:
The method of claim 21, wherein the parameters of the assigned numerical filters represent changes to a synaptic current that result when corresponding dendritic synapses are moved to respective somas. (mental process – a person can recognize that parameters of the assigned numerical filters represent changes to a synaptic current that result when corresponding dendritic synapses are moved to respective somas.)
With respect to claim 28:
Step 2A – Prong 1:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises: grouping synapses in a morphologically-detailed model of the biological brain into multiple groups according to characteristics and positions of the synapses; (mental process – a person can manually group synapses in a morphologically-detailed model of the biological brain into multiple groups according to characteristics and positions of the synapses with the assistance of a pen/paper.)
selecting a representative synapse within each group of the multiple groups; (mental process – a person can manually select a representative synapse within each group of the multiple groups with the assistance of a pen/paper.)
estimating parameters of numerical filters for each selected representative synapse; (mental process – a person can manually estimate parameters of numerical filters for each selected representative synapse with the assistance of a pen/paper.)
and assigning the estimated parameters of numerical filters for each selected representative synapse to other synapses in the same group. (mental process – a person can manually assign the estimated parameters of numerical filters for each selected representative synapse to other synapses in the same group with the assistance of a pen/paper.)
With respect to claim 29:
Step 2A – Prong 1:
The method of claim 28, wherein the characteristics and positions of the synapses comprise dendritic compartment and synapse type. (mental process – a person can recognize that characteristics and positions of the synapses comprise dendritic compartment and synapse type.)
With respect to claim 30:
Step 2A – Prong 1:
The method of claim 21, wherein assigning the estimated parameters to the numerical filters in the neural network model comprises replacing connections between neurons in the neural network model with filtered connections between neurons in the neural network model. (mental process – a person can recognize that assigning the estimated parameters to the numerical filters in the neural network model comprises replacing connections between neurons in the neural network model with filtered connections between neurons in the neural network model.)
With respect to claim 31:
Step 2A – Prong 1:
The method of claim 21, further comprising, after assigning the estimated parameters to the numerical filters in the neural network model, simulating activity in a morphologically-detailed model of the biological brain using the neural network model. (mental process – a person can recognize that after assigning the estimated parameters to the numerical filters in the neural network model, simulating activity in a morphologically-detailed model of the biological brain using the neural network model.)
With respect to claim 32:
Step 2A – Prong 1:
A system comprising one or more computers and one or more storage devices that implement a neural network model comprising a plurality of interconnected neurons, wherein each neuron is associated with a respective numerical filter that filter inputs between respective neurons in the neural network model, wherein parameters of the numerical filter capture a topographic mapping from a brain region in which the neuron is positioned. (mental process – a person can recognize that each neuron is associated with a respective numerical filter that filter inputs between respective neurons in the neural network model, wherein parameters of the numerical filter capture a topographic mapping from a brain region in which the neuron is positioned.)
Claims 33-36 are rejected on the same grounds under 35 U.S.C. 101 as claims 24-27 as they are substantially similar, respectively. Mutatis mutandis.
Claim 37 is rejected on the same grounds under 35 U.S.C. 101 as claims 21 and 31 as they are substantially similar. Mutatis mutandis.
Claims 38-40 are rejected on the same grounds under 35 U.S.C. 101 as claims 24-26 as they are substantially similar, respectively. 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 21-24, 30-33, 37-38 are rejected under 35 U.S.C. 103 as being unpatentable over Markram et al. (“Reconstruction and Simulation of Neocortical Microcircuitry”) hereinafter known as Markram in view of Wybo et al. (“A sparse reformulation of the Green’s function formalism allows efficient simulations of morphological neuron models”) hereinafter known as Wybo in view of Pozzorini et al. (“Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models”) hereinafter known as Pozzorini.
Regarding independent claim 21, Markram teaches:
A method for integrating topographic mappings into a neural network model of a biological brain, the method comprising: providing the neural network model, wherein the neural network model comprises a plurality of interconnected neurons; (Markram [Page 456, Col. 1, Paragraph 1]: “When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form about 8 million connections with about 37 million synapses.” Markram teaches that the neurons form millions of connections with each other.)
estimating parameters of numerical filters that filter inputs between respective neurons in the neural network model, … (Markram [Page 468, Col. 2, Paragraph 3]: “s-types of specific connections were determined from the combination of their pre- and postsynaptic me-types… five rules to predict s-types for broad classes of connections” Markram teaches the s-types of the connections, which are parameters that give the dynamics of the connection. These are derived from the me-types, which are functional qualities of the neurons. Indeed, the neurons’ functional roles represent the connection parameters.)
Markram does not explicitly teach:
… wherein the parameters of the assigned numerical filters represent positions and functional roles of the neurons in the biological brain;
However, Wybo teaches:
… wherein the parameters of the assigned numerical filters represent positions and functional roles of the neurons in the biological brain; (Wybo [Page 2, Equation 1]: Wybo teaches that the positions x_i, x_j, are factors in the Green’s function, showing that the specific position has a role in the output.)
Markram and Wybo are in the same field of endeavor as the present invention, as the references are directed to modeling/simulating the brain or nerve cells. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine parameters between connections of neurons in their functional role as taught in Markram with parameters representing the specific positions of the neurons as taught in Wybo. Wybo provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Markram to include teachings of Wybo because the combination would allow for the parameters to capture both the positions and functional roles of the neurons. This has the potential benefit of improving the accuracy of the biological model, as the role/function of the neuron as well as the positional importance of the neuron can be parameterized.
Markram and Wybo do not explicitly teach:
and assigning the estimated parameters to the numerical filters in the neural network model.
However, Pozzorini teaches:
and assigning the estimated parameters to the numerical filters in the neural network model. (Pozzorini [Page 1, Paragraph 1]: “using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. … A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.” Pozzorini teaches using convex optimization to fit a model with limited data, which is equivalent to assigning estimated parameters to numerical filters of the model.)
Pozzorini is in the same field as the present invention, since it is directed to modeling biological neurons. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine parametrizing the role/function of the neuron and its positional importance as taught in Markram as modified by Wybo with fitting estimated parameters to the filters of the model as taught in Pozzorini. Pozzorini provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Markram as modified by Wybo to include teachings of Pozzorini because the combination would allow for the parameters to be best fitted using optimization techniques. This has the potential benefit of producing an efficient but accurate training system for the model.
Regarding dependent claim 22, Markram, Wybo, and Pozzorini teach:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises estimating, for each of one or more individual neurons in the neural network model and using direct filter extraction, parameters of the numerical filters from a modelled behavior of the individual neuron in a morphologically-detailed model of the biological brain. (Pozzorini [Page 15, Fig. 8 description]: “Staining of a biocytin-filled L5 pyramidal neuron included in this study. … Gray lines show the results from individual neurons” Pozzorini teaches that the gray lines come from individual neurons. That is, each neuron’s filter parameters are computed from that one neuron’s own data. Pozzorini [Page 10, Paragraph 3]: “In contrast to point-neuron models, detailed biophysical models account for the intricate morphology of both dendritic and axonal arborizations” Pozzorini teaches that a morphologically-detailed model instead of point neuron models.)
The reasons to combine are substantially similar to those of claim 21.
Regarding dependent claim 23, Markram, Wybo, and Pozzorini teach:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises estimating, for each of one or more groups of neurons in the neural network model and using implicit filter extraction, parameters of the numerical filters from a modelled behavior of the group of neurons in a morphologically-detailed model of the biological brain. (Pozzorini [Page 15, Fig. 8 description]: “GIF model parameters extracted from ten L5 pyramidal neurons. Average filters are shown in red. … Bar plots indicate the mean and one standard deviation across neurons.” Pozzorini teaches an average filter and a mean across neurons, indicating that there are filter parameters that are computed from a group of neurons rather than from a single cell. Pozzorini [Page 16, Paragraph 1]: “parameters describing the passive properties of the membrane revealed the presence of cell-to-cell variability” Pozzorini teaches a cell-to-cell variability, which is treated as a noise variable in the context of an average on the scale of the group.)
The reasons to combine are substantially similar to those of claim 21.
Regarding dependent claim 24, Markram, Wybo, and Pozzorini teach:
The method of claim 21, wherein the parameters of the assigned numerical filters indicate whether corresponding synapses are inhibitory or excitatory. (Markram [Page 486, Col. 1, Paragraph 6]: “Excitatory synaptic transmission was modeled using both AMPA and NMDA receptor kinetics … Inhibitory synaptic transmission was modeled with a combination of GABAA and GABAB receptor kinetics” Markram teaches that the parameters indicate whether the synapses are excitatory or inhibitory.)
The reasons to combine are substantially similar to those of claim 21.
Regarding dependent claim 30, Markram, Wybo, and Pozzorini teach:
The method of claim 21, wherein assigning the estimated parameters to the numerical filters in the neural network model comprises replacing connections between neurons in the neural network model with filtered connections between neurons in the neural network model. (Wybo [Page 30, last paragraph continued to next page]: “seeking ways of reducing the cost of simulating these cells to be able to use them in large scale network simulations … achieve this by drastically reducing the number of compartments … With the SGF formalism, inputs that would otherwise be grouped in a small number of compartments may now be grouped at a small number of input locations, while the response properties induced by the neuronal morphology would remain unchanged” Wybo teaches that the Green’s function allows for a reduced set of filtered connections that still produce the same response.)
The reasons to combine are substantially similar to those of claim 21.
Regarding dependent claim 31, Markram, Wybo, and Pozzorini teach:
The method of claim 21, further comprising, after assigning the estimated parameters to the numerical filters in the neural network model, simulating activity in a morphologically-detailed model of the biological brain using the neural network model. (Pozzorini [Page 12, Paragraph 3]: “Both models achieved a similar performance and were able to predict around 80% of the spikes emitted by the DBM” Pozzorini teaches that the Generalized Integrate-and-Fire model does a relatively accurate job of predicting the spikes from the detailed biophysical model.)
The reasons to combine are substantially similar to those of claim 21.
Regarding independent claim 32, Markram, Wybo, and Pozzorini teach:
A system comprising one or more computers and one or more storage devices that implement a neural network model comprising a plurality of interconnected neurons, wherein each neuron is associated with a respective numerical filter that filter inputs between respective neurons in the neural network model, wherein parameters of the numerical filter capture a topographic mapping from a brain region in which the neuron is positioned. (“Wybo [Page 2, Equation 1]: Wybo teaches that the positions x_i, x_j, are factors in the Green’s function, showing that the specific position has a role in the output.)
The reasons to combine are substantially similar to those of claim 21.
Claim 33 is rejected on the same grounds under 35 U.S.C. 103 as claim 24 as they are substantially similar, respectively. Mutatis mutandis.
Claim 37 is rejected on the same grounds under 35 U.S.C. 103 as claims 21 and 31 as they are substantially similar, respectively. Mutatis mutandis.
Claim 38 is rejected on the same grounds under 35 U.S.C. 103 as claim 24 as they are substantially similar, respectively. Mutatis mutandis.
Claims 25-29. 34-36, 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Markram in view of Wybo in view of Pozzorini in view of Marasco et al. (“Fast and accurate low-dimensional reduction of biophysically detailed neuron models”) hereinafter known as Marasco.
Regarding dependent claim 25, Markram, Wybo, and Pozzorini teach:
The method of claim 21,
Markram, Wybo, and Pozzorini do not explicitly teach:
wherein the parameters of the assigned numerical filters indicate whether corresponding synapses are located on an apical or basal dendrite.
However, Marasco teaches:
wherein the parameters of the assigned numerical filters indicate whether corresponding synapses are located on an apical or basal dendrite. (Marasco [Page 5, Col. 2, Paragraph 1]: “cluster 4: apical dendrites (oblique) at distance d <= 100 micrometers from soma … cluster 7: basal dendrites (proximal) at distance d <= 100 micrometers from soma” Marasco teaches that the synapses are located on apical or basal dendrites and that which cluster it’s on is based on the parameters.)
Marasco is in the same field as the present invention, since it is directed to modeling neurons by clustering. It would have been obvious, before the effective filing date of the claimed invention, to a person of ordinary skill in the art, to combine parameters fitting the parameters to the model using optimization techniques as taught in Markram as modified by Wybo as modified by Pozzorini with clustering the neurons based on characteristics of the synapses as taught in Marasco. Marasco provides this additional functionality. As such, it would have been obvious to one of ordinary skill in the art to modify the teachings of Markram as modified by Wybo as modified by Pozzorini to include teachings of Marasco because the combination would allow for the parameters to reflect which characteristic of the synapse the neuron is most like after grouping. This has the potential benefit of improving the accuracy of modeling of neurons that are of a specific type/group.
Regarding dependent claim 26, Markram, Wybo, Pozzorini, and Marasco teach:
The method of claim 21, wherein the parameters of the assigned numerical filters represent a distance between corresponding synapses and somas. (Marasco [Page 5, Col. 2, Paragraph 1]: “cluster 4: apical dendrites (oblique) at distance d <= 100 micrometers from soma … cluster 7: basal dendrites (proximal) at distance d <= 100 micrometers from soma” Marasco teaches that the clusters are defined by a threshold distance from the synapses to the somas.)
The reasons to combine are substantially similar to those of claim 25.
Regarding dependent claim 27, Markram, Wybo, Pozzorini, and Marasco teach:
The method of claim 21, wherein the parameters of the assigned numerical filters represent changes to a synaptic current that result when corresponding dendritic synapses are moved to respective somas. (Marasco [Page 6, Col. 1, Paragraph 3]: “this problem could be solved by rescaling (and repositioning) the synaptic conductances in such a way to maintain the same signal propagation as in the full model. We found that a good way to achieve this goal was to take into account the axial path resistance” Marasco teaches repositioning the synaptic conductances/current. Marasco [Page 6, Col. 2, second and third equations]: “the peak synaptic conductance is scaled as …” Marasco teaches that the original current/conductance is rescaled using a scaling factor parameter.)
The reasons to combine are substantially similar to those of claim 25.
Regarding dependent claim 28, Markram, Wybo, Pozzorini, and Marasco teach:
The method of claim 21, wherein estimating the parameters of the numerical filters comprises: grouping synapses in a morphologically-detailed model of the biological brain into multiple groups according to characteristics and positions of the synapses; (Marasco [Page 2, Col. 1, Paragraph 2]: “we noted that the morphology and distribution of active properties for these cells allowed us to define nine functional regions (clusters) composed by a variable number of dendritic compartments” Marasco teaches grouping synapses into clusters/groups based on the properties of the cells.)
selecting a representative synapse within each group of the multiple groups; (Marasco [Page 2, Col. 1, Paragraph 2]: “the first step in the reduction algorithm is to map each cluster into an equivalent single compartment. This is carried out by using merging rules based only on the passive, active, and morphological properties of the full neuron without any fitting or tuning procedure.” Marasco teaches mapping each cluster into an equivalent single compartment, which is selective a representative synapses compartment within each group.)
estimating parameters of numerical filters for each selected representative synapse; (Marasco [Page 5, Col. 2, Paragraph 3]: “The second step is to map each functional region into a single compartment in the reduced model and calculate its morphological and passive properties” Marasco teaches calculating the morphological and passive properties/parameters for each compartment.)
and assigning the estimated parameters of numerical filters for each selected representative synapse to other synapses in the same group. (Marasco [Page 5, Col. 2, Paragraph 10]: “for each cluster we determine a scaling factor, fact^{eq}, which depends on the presence of synaptic inputs” Marasco teaches that the scaling factor and other properties are computed once and then applied for every other synapse in the group.)
The reasons to combine are substantially similar to those of claim 25.
Regarding dependent claim 29, Markram, Wybo, Pozzorini, and Marasco teach:
The method of claim 28, wherein the characteristics and positions of the synapses comprise dendritic compartment … (Marasco [Page 5, Col. 2, Paragraph 2]: Marasco shows that there are 8 clusters – each cluster is based on dendric compartment type, showing that the characteristics and positions are based on this typing.)
… and synapse type. (Markram [Page 468, Col. 2, Paragraph 3]: “s-types of specific connections were determined from the combination of their pre- and postsynaptic me-types” Markram teaches that the s-types, which are the types that the synapses are categorized into, determine which parameters a synapse is assigned.)
The reasons to combine are substantially similar to those of claim 25.
Claims 34-36 are rejected on the same grounds under 35 U.S.C. 103 as claims 25-27 as they are substantially similar, respectively. Mutatis mutandis.
Claims 39-40 are rejected on the same grounds under 35 U.S.C. 103 as claims 25-26 as they are substantially similar, respectively. Mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYU HYUNG HAN whose telephone number is (703) 756-5529. The examiner can normally be reached on MF 9-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Kyu Hyung Han/
Examiner
Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
Prosecution Insights