DETAILED ACTION
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 .
Status of Claims
This Office Action is in response to the communication filed on 07 Dec 2022.
Claims 1-18 are being considered on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07 December 2022 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, initialed and dated copies of Applicant's IDS form 1499 is attached to the instant Office action.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Nessler B, Pfeiffer M, Buesing L, Maass W (“Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity.” 2013. PLOS Computational Biology 9(4): e1003037. https://doi.org/10.1371/journal.pcbi.1003037; hereinafter “Nessler”) in view of Sinyavskiy, et. al. (US 2014/0032458 A1; hereinafter, “Sinyavskiy”)
Regarding Claim 1, Nessler teaches:
calculating a time length maximizing a likelihood probability distribution or a posterior probability distribution (Nessler, pg. 2, right column: “We show that in our spiking soft-WTA circuit each output spike can be viewed as an application of the
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-step of EM. The subsequent modification of the synaptic weights between the presynaptic input neurons and the very neuron that has fired the postsynaptic spike according to STDP can be viewed as a move in the direction of the
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-step of a stochastic online EM procedure. This procedure strives to create optimal internal models for high-dimensional spike inputs by maximizing their
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-likelihood.”)
[the time length] based on a multi-dimensional variable influencing a change in a time length used by a neuromorphic processing unit to perform an operation; (Nessler, pg. 5, left column and Fig. 2: “this update rule is exactly equivalent to the simple STDP rule (solid red curve) in Fig. 2 for the case of the pairing of one pre- and one postsynaptic spike. The dependence on the presynaptic activity
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is reflected directly by the time difference
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between the pre- and the postsynaptic spikes. According to this rule positive updates are only performed if the presynaptic neuron fired in a time window of
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ms before the postsynaptic spike” Examiner notes Nessler teaches a multi-dimensional variable in the form of a time window wherein the time window involves analysis of pre and post synaptic activity as well as the timing of each and the time differences between each).
generating a lookup table based on the multi-dimensional variable and the time length maximizing the likelihood probability distribution or the posterior probability distribution for the multi-dimensional variable; and (Nessler, pg. 5, right column: “This is done by sampling from the prior distribution, and then sampling the 's, which depend on and can be generated according to the conditional probability tables” Examiner notes conditional probability tables are lookup tables).
Nessler does not explicitly disclose but Sinyavskiy teaches:
A method for synchronizing neuromorphic processing units, comprising: (Sinyavskiy, para. 0161: “FIGS. 7-8 illustrate timing of the updates and EDCC component selection in accordance with one or more implementations. The panel 740 in FIG. 7 presents a sequence of EDCC components 741, 742, 744, 746, 748, such as, for example, described above with respect to Eqn. 20 and FIG. 6. The panel 750 in FIG. 7 illustrates event sequence, comprising for example, neuron inputs 752, 754, 756, 758, 759 (such as feed-forward input and/or a reinforcement spikes). In one implementation, (not shown) the event clock and the EDCC components computation clock may be synchronized and selected to be updated at regular network update intervals, for example 1 ms intervals.”)
updating the lookup table based on the time length used by the neuromorphic processing unit to perform the operation and the time length maximizing the likelihood probability distribution or the posterior probability distribution (Sinyavskiy, para.0057 and claim 2: “In some implementations, the linear interface dynamic process may be configured to be periodically updated at a time interval. The decay window may comprise two or more the time intervals. The determining of the plurality of interface change components may be effectuated via a look-up table comprising two or more entries associated with the two or more of the time intervals. Individual ones of the plurality of interface change components may be configured based on the state of the node. The look-up table may be determined for two or more of the updates.” “the update further comprises a determination of a plurality of eligibility traces, individual ones of the plurality of eligibility traces being associated with a given one of the plurality of connections and comprising a temporary record of occurrence of at least one of the one or more inputs on the given one of the plurality of connections”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler. Nessler teaches STDP approximates one of the most powerful learning methods in machine learning, Expectation-Maximization (EM); Sinyavskiy teaches a framework to describe the connections using a linear synaptic dynamic process. One of ordinary skill would have been motivated to combine the teachings of Sinyavskiy into Nessler in order to provide substantial reduction in a number of computations that may be required in order to implement synaptic updates for a spiking neuron comprising many synaptic connection (Sinyavskiy, para. 0281).
Regarding Claim 10, Nessler teaches:
wherein the processor is configured to calculate a time length maximizing a likelihood probability distribution or a posterior probability distribution (Nessler, pg. 2, right column: “We show that in our spiking soft-WTA circuit each output spike can be viewed as an application of the
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-step of EM. The subsequent modification of the synaptic weights between the presynaptic input neurons and the very neuron that has fired the postsynaptic spike according to STDP can be viewed as a move in the direction of the
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15
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-step of a stochastic online EM procedure. This procedure strives to create optimal internal models for high-dimensional spike inputs by maximizing their
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-likelihood.”)
[the time length] based on a multi-dimensional variable influencing a change in a time length used by a neuromorphic processing unit to perform an operation, (Nessler, pg. 5, left column and Fig. 2: “this update rule is exactly equivalent to the simple STDP rule (solid red curve) in Fig. 2 for the case of the pairing of one pre- and one postsynaptic spike. The dependence on the presynaptic activity
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is reflected directly by the time difference
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between the pre- and the postsynaptic spikes. According to this rule positive updates are only performed if the presynaptic neuron fired in a time window of
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8
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ms before the postsynaptic spike” Examiner notes Nessler teaches a multi-dimensional variable in the form of a time window wherein the time window involves analysis of pre and post synaptic activity as well as the timing of each and the time differences between each).
generate a lookup table based on the multi-dimensional variable and the time length maximizing the likelihood probability distribution or the posterior probability distribution for the multi-dimensional variable, and (Nessler, pg. 5, right column: “This is done by sampling from the prior distribution, and then sampling the 's, which depend on and can be generated according to the conditional probability tables” Examiner notes conditional probability tables are lookup tables)
Nessler does not specifically disclose but Sinyavskiy teaches:
An apparatus for synchronizing neuromorphic processing units, comprising: (Sinyavskiy, para. 0161: “FIGS. 7-8 illustrate timing of the updates and EDCC component selection in accordance with one or more implementations. The panel 740 in FIG. 7 presents a sequence of EDCC components 741, 742, 744, 746, 748, such as, for example, described above with respect to Eqn. 20 and FIG. 6. The panel 750 in FIG. 7 illustrates event sequence, comprising for example, neuron inputs 752, 754, 756, 758, 759 (such as feed-forward input and/or a reinforcement spikes). In one implementation, (not shown) the event clock and the EDCC components computation clock may be synchronized and selected to be updated at regular network update intervals, for example 1 ms intervals.”)
a memory configured to store a control program for synchronizing neuromorphic processing units; and (Sinyavskiy, para. 0267: “The neuromorphic processing system 1130 of FIG. 11B may comprises a plurality of processing blocks (micro-blocks) 1140 where individual micro cores may comprise a computing logic core 1132 and a memory block 1134. The logic core 1132 may be configured to implement various aspects of neuronal node operation, such as the node model, and synaptic update rules (e.g., the I-STDP) and/or other tasks relevant to network operation.”)
a processor configured to execute the control program stored in the memory, (Sinyavskiy, para. 0049: “The system may include one or more processors configured to execute one or more computer program modules to perform one or more operations.”)
update the lookup table based on the time length used by the neuromorphic processing unit to perform the operation and the time length maximizing the likelihood probability distribution or the posterior probability distribution. (Sinyavskiy, para.0057 and claim 2: “In some implementations, the linear interface dynamic process may be configured to be periodically updated at a time interval. The decay window may comprise two or more the time intervals. The determining of the plurality of interface change components may be effectuated via a look-up table comprising two or more entries associated with the two or more of the time intervals. Individual ones of the plurality of interface change components may be configured based on the state of the node. The look-up table may be determined for two or more of the updates.” “the update further comprises a determination of a plurality of eligibility traces, individual ones of the plurality of eligibility traces being associated with a given one of the plurality of connections and comprising a temporary record of occurrence of at least one of the one or more inputs on the given one of the plurality of connections”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claims 2 and 11, Nessler as modified teaches claims 1 and 10 above, Nessler further teaches:
a time length (
X
e
) maximizing a likelihood probability distribution or a posterior probability distribution for the multi-dimensional variable
(
θ
)
. (Nessler, pg. 2, right column: “We show that in our spiking soft-WTA circuit each output spike can be viewed as an application of the
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10
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-step of EM. The subsequent modification of the synaptic weights between the presynaptic input neurons and the very neuron that has fired the postsynaptic spike according to STDP can be viewed as a move in the direction of the
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15
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-step of a stochastic online EM procedure. This procedure strives to create optimal internal models for high-dimensional spike inputs by maximizing their
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-likelihood.”)
wherein the lookup table includes (
θ
,
X
e
) pairs formed using a multi-dimensional variable (
θ
) influencing a change in a time length (
X
r
) used by the neuromorphic processing unit to complete data processing and exchange and (Sinyavskiy, para. 0057: “In some implementations, the linear interface dynamic process may be configured to be periodically updated at a time interval. The decay window may comprise two or more the time intervals. The determining of the plurality of interface change components may be effectuated via a look-up table comprising two or more entries associated with the two or more of the time intervals. Individual ones of the plurality of interface change components may be configured based on the state of the node. The look-up table may be determined for two or more of the updates.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 3, and 12, Nessler as modified teaches claims 2 and 11 above, Nessler further teaches:
a time length (
X
e
,
h
) maximizing a likelihood probability distribution or a posterior probability distribution for the multi-dimensional variable (
θ
h
). (Nessler, pg. 2, right column: “We show that in our spiking soft-WTA circuit each output spike can be viewed as an application of the
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10
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-step of EM. The subsequent modification of the synaptic weights between the presynaptic input neurons and the very neuron that has fired the postsynaptic spike according to STDP can be viewed as a move in the direction of the
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15
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-step of a stochastic online EM procedure. This procedure strives to create optimal internal models for high-dimensional spike inputs by maximizing their
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-likelihood.”)
Nessler does not specifically disclose but Sinyavskiy teaches:
wherein the lookup table includes (
θ
h
,
X
e
,
h
) pairs formed using a multi-dimensional variable (
θ
h
) influencing changes in respective time lengths (
X
r
,
h
) used by multiple neuromorphic processing units to complete sequential multi-step data processing and data exchange and (Sinyavskiy, para. 0057: “In some implementations, the linear interface dynamic process may be configured to be periodically updated at a time interval. The decay window may comprise two or more the time intervals. The determining of the plurality of interface change components may be effectuated via a look-up table comprising two or more entries associated with the two or more of the time intervals. Individual ones of the plurality of interface change components may be configured based on the state of the node. The look-up table may be determined for two or more of the updates.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 4, and 13, Nessler as modified teaches claims 3 and 12 above, Sinyavskiy further teaches:
wherein the time length used by the neuromorphic processing unit to perform the operation is determined to be a sum of respective time lengths (
X
r
,
h
) used by the multiple neuromorphic processing units to complete sequential multi-step data processing and data exchange. (Sinyavskiy, para. 0103: “n accordance with the principles of the disclosure, multiple synaptic updates may be configured to be executed on per neuron basis, as opposed to per-synapse basis of prior art. The cumulative synaptic plasticity update in accordance with some implementations may be factored (decomposed) into multiple event-dependent connection change (EDCC) components. The EDCC components may be configured to describe synapse plasticity change due to neuronal input spike (i.e., the spike transmitted by a synapse from a pre-synaptic neuron into a post-synaptic neuron) occurring at time ti≦tupdate. In order to effectuate factoring of the synaptic updates, at individual update instances tupdate (e.g., cyclic and/or on-demand), two or more EDCC components may be computed, with individual components corresponding to one prior network state update time interval ti. The number of EDCC components computed may be configured based on (i) the plasticity rule decay time scale used by the network, and (ii) the network update interval Δt. By way of illustration, if the plasticity decay time-scale T is 20 ms and the network state is updated at 1 ms intervals, then at individual synaptic update events at time t, a number nT=T/Δt of EDCC components (nT=20 in one or more implementations) may be computed, with individual components corresponding to the plasticity change due to input (pre-synaptic) spike occurring at time ti=t−(i−1)×Δt, i={1, . . . , nT}. It is noteworthy, that the nT EDCC components may be computed once for all synapses associated with the neuron, and the occurrence times of input spikes within the time interval (t−T) prior to updates may be used to reference appropriate EDCC component.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 5 and 14, Nessler as modified teaches claims 3 and 12 above, Sinyavskiy further teaches:
wherein the lookup table comprises a first lookup table including the (
θ
,
X
e
) pairs and a second lookup table including the (
θ
h
,
X
e
,
h
) pairs, and the first and second lookup tables are individually managed by an internal memory or an external memory of each neuromorphic processing unit. (Sinyavskiy, para. 0057: “The determining of the plurality of interface change components may be effectuated via a look-up table comprising two or more entries associated with the two or more of the time intervals. Individual ones of the plurality of interface change components may be configured based on the state of the node. The look-up table may be determined for two or more of the updates.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 6 and 15, Nessler as modified teaches claims 1 and 10 above, Sinyavskiy further teaches:
wherein the lookup table is constructed and updated based on at least one of linear/nonlinear programming, Markov chain Monte-Carlo (MCMC) methodology, Laplace approximation, regression analysis, a random process, an artificial neural network, gradient descent, a Newton method or a Kalman filter, or a combination thereof. (Sinyavskiy, para. 0206: “In one or more implementations, EDCC components may comprise one or more eligibility trace configured for implementing reward-based exploration during reinforcement learning. In one or more implementations, the exploration may comprise potentiation of inactive neurons, as described for example a co-owned U.S. patent application Ser. No. 13/489,280 entitled “APPARATUS AND METHODS FOR REINFORCEMENT LEARNING IN ARTIFICIAL NEURAL NETWORKS”, filed Jun. 5, 2012, [client reference BC201204A] incorporated supra”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 7 and 16, Nessler as modified teaches claims 1 and 10 above, Nessler further teaches:
the time length maximizing the likelihood probability distribution or the posterior probability distribution. (Nessler, pg. 2, right column: “We show that in our spiking soft-WTA circuit each output spike can be viewed as an application of the
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-step of EM. The subsequent modification of the synaptic weights between the presynaptic input neurons and the very neuron that has fired the postsynaptic spike according to STDP can be viewed as a move in the direction of the
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15
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-step of a stochastic online EM procedure. This procedure strives to create optimal internal models for high-dimensional spike inputs by maximizing their
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21
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-likelihood.”)
Nessler does not explicitly disclose but Sinyavskiy teaches:
wherein whether the lookup table is to be updated is determined based on a difference between the time length used by the neuromorphic processing unit to perform the operation and (Sinyavskiy, para. 0046, 0061 and 0062: “One or more interface change components may be determined based on a difference between (i) the respective time period and (ii) the indication time instance.” “In some implementations, the update may be delayed until a next regular time interval occurring subsequent to occurrence of the data item of the one or more data items.” “In some implementations, the node dynamic process may comprise a reinforcement learning process configured to produce an outcome. The update of the plurality of parameters may be capable of aiding the outcome being produced. The indication may comprise a reinforcement spike generated based on an evaluation of node output versus the outcome.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 8 and 17, Nessler as modified teaches claims 1 and 10 above, Sinyavskiy further teaches:
wherein the multi-dimensional variable includes at least one of state information of the neuromorphic processing unit, a method for exchanging data between neuromorphic processing units, or a policy, or a combination thereof. (Sinyavskiy, para. 0102: “The present disclosure provides, among other things, a computerized apparatus and methods for facilitating learning spiking neuron networks by, inter alia, implementing efficient synaptic updates. In one or more implementations, the network may comprise linear synaptic dynamic process, characterized by stable equilibrium. The state of neurons and synapses within the network may be updated, based on inputs and outputs to/from neurons. In some implementations, the updates may be implemented at regular time intervals Δt. In one or more implementations, the updates may be implemented on-demand, based on network activity (e.g., neuronal output and/or input) so as to further reduce computational load associated with the synaptic updates.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Regarding Claim 9 and 18, Nessler as modified teaches claims 8 and 17 above, Sinyavskiy further teaches:
The method of claim 8, wherein the state information of the neuromorphic processing unit includes at least one of an amount and a structure of input data, a neuron state variable value or information about a connection structure between neuromorphic processing units, or a combination thereof. (Sinyavskiy, para. 0140: “In one or more implementations, the dimension of the basis vector {right arrow over (b)}m(t) may be selected to match the dimension of the estate vector Si(t) in Eqn. 18.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Sinyavskiy into Nessler, as modified, as set forth above with respect to claim 1.
Prior Art
Silva, Miguel (“Neuromorphic Neural Networks” (August 8, 2022). Available at SSRN: https://ssrn.com/abstract=4184956 or http://dx.doi.org/10.2139/ssrn.4184956) an asynchronous data exchange network and time-independent clusters of neural networks to form one single global deep neural network suitable to be implemented on neuromorphic computing technologies.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sally T. Ley whose telephone number is (571)272-3406. The examiner can normally be reached Monday - Thursday, 10:00am - 6:00pm ET.
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/STL/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151