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 § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 14-16, 18, 20, 21, 23, 31 are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013.
With regard to independent claim 1,
Wierzynski teaches, “A method performed by a transmitting node
of a neural network for congestion level control, the method comprising:” (Paragraph 0087-0088; EN: This denotes a method for managing a neural network, where the neuron model can monitor congestion indications and modify spike distribution). “sending, to a receiving node of the neural network, a plurality of sequences of data” (Paragraph 0026-0027, 0034-0035; EN: This denotes a neural system that contains various levels of neurons, and the neurons of one level can send an input signal, which can be real-valued and/or complex-valued, to the neurons of another level and generate a spike output to be transferred to the next neuron). “the sequences of data are being temporally encoded according to an encoding configuration;” (Paragraph 0028-0029, 0034-0035, 0042, 0047; EN: This denotes that the output spike that is generated when a neuron is fired can contain information about the time of the spike and the time of the spike relative to another spike or spikes).
“sending, to the receiving node, an indication of the encoding configuration, enabling the sequences of data to be decoded;” (Paragraph 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information sent to a physical information unit (PHIT) router via a super neuron). “…in decoding the sequences of data; and” (Paragraph 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information, which can be a synaptic event, sent to a physical information unit (PHIT) router via a super neuron. The output spike information is then provided to a Cache Line Segment (CLS) Fetch/Refetch Manager and the CLS fetches the synapse state information from the DRAM, which is then routed for processing based on synaptic event and the connectivity information). “adapting the encoding configuration…” (Paragraph 0071-0073; EN: This denotes that the congestion controller can modify spike distribution based on a received indication of congestion, where the controller can increase or decrease the spike drop rate).
However, Wierzynski fails to disclose “receiving, from the receiving node, feedback comprising an indication of an error experienced…” and “…based on the feedback”.
Sinyavskiy teaches “receiving, from the receiving node, feedback comprising an indication of an error experienced…” (Fig. 2, Col. 11, Lines 1-42; EN: This denotes that the second layer neuron, which can determine an output that corresponds to the output of the first layer neuron, can determine a feedback signal. This feedback signal, which can comprise an error signal, can be communicated from the second layer neuron to the first layer neuron through feed-forward and feedback connections). “…based on the feedback” (Fig. 2, Col. 11, Lines 1-42; EN: This denotes that that the second layer neuron can determine and send a feedback signal to the first neuron layer).
Wierzynski and Sinyavskiy are considered to be analogous to the claimed invention due to the fact that they are both generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski with the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy. One would be motivated to do so to improve the congestion modification within neural networks and improve the efficiency of spikes.
With regard to dependent claim 2,
Wierzynski teaches, “The method of claim 1, wherein the neural network comprises a spiking neural network, and wherein the sequences of data comprise sequences of one or more spikes” (Paragraph 0026-0027, 0034-0035; EN: This denotes an artificial neural system or artificial neural network that contains various levels of neurons, where the neurons of one level can send an input signal to the neurons of another level and generate a spike output to be transferred to the next neuron. The examiner notes that this neural network communicates using spikes and therefore, under BRI of the term spiking neural network, Wierzynski teaches the spiking neural network).
With regard to dependent claim 3,
Wierzynski teaches, “The method of claim 1, wherein adapting the encoding configuration comprises adapting a rate at which information is encoded temporally for transmission” (Paragraph 0071; EN: This denotes the modification of spike distribution, which can include modifying spike drop rates).
With regard to dependent claim 14,
Wierzynski teaches, “The method of claim 1, wherein the sequences of data are sent to the receiving node using one or more of: radio transmission; optical transmission; free space visible light transmission; and electrical transmission” (Paragraph 0036; EN: This denotes that the neurons can be emulated by an electrical circuit that utilizes electrical connections with synaptic circuits, software code emulated by an electrical circuit, an analog electrical circuit, a digital electrical circuit, or a mixed signal electrical circuit utilizing analog and digital components).
With regard to independent claim 15,
Wierzynski teaches, “A method performed by a receiving node of a neural network for congestion level control, the method comprising:” (Paragraph 0087-0088; EN: This denotes a method for managing a neural network, where the neuron model can monitor congestion indications and modify spike distribution). “receiving, from a transmitting of a neural network and, a plurality of sequences of data, the sequences of data are being temporally encoded according to an encoding configuration;” (Paragraph 0026-0029, 0034, 0042, 0047; EN: This denotes a neural system that contains various levels of neurons, where the neurons of one level can send an input signal to the neurons of another level and generate a spike output to be transferred to the next neuron. The output spike that is generated when a neuron is fired can contain information about the time of the spike and the time of the spike relative to another spike or spikes).
“receiving, from the transmitting node, an indication of the encoding configuration;” (Paragraph 0026-0029, 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information sent to a physical information unit (PHIT) router via a super neuron). “decoding the sequences of data using the indication of the encoding configuration to obtain decoded data values for the sequences of data; and” (Paragraph 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information, which can be a synaptic event, sent to a physical information unit (PHIT) router via a super neuron. The output spike information is then provided to a Cache Line Segment (CLS) Fetch/Refetch Manager and the CLS fetches the synapse state information from the DRAM, which is then routed for processing based on synaptic event and the connectivity information). “…associated with the decoding of the sequences of data” (Paragraph 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information, which can be a synaptic event, sent to a physical information unit (PHIT) router via a super neuron. The output spike information is then provided to a Cache Line Segment (CLS) Fetch/Refetch Manager and the CLS fetches the synapse state information from the DRAM, which is then routed for processing based on synaptic event and the connectivity information).
However, Wierzynski fails to explicitly teach “sending, to the transmitting node, feedback comprising an indication of an error…”.
Sinyavskiy teaches, “sending, to the transmitting node, feedback comprising an indication of an error” (Fig. 2, Col. 11, Lines 1-42; EN: This denotes that the second layer neuron, which can determine an output that corresponds to the output of the first layer neuron, can determine a feedback signal. This feedback signal, which can comprise an error signal, can be communicated from the second layer neuron to the first layer neuron through feed-forward and feedback connections).
With regard to dependent claim 16,
The rest of this claim is similar in scope to claim 2 and is rejected under
a similar rationale.
With regard to dependent claim 18,
Wierzynski fails to explicitly teach “The method of claim 15, further comprising estimating the error, wherein the error comprises a difference between the sequences of data as received by the receiving node, and the sequences of data as transmitted by the transmitting node”.
Sinyavskiy teaches, “The method of claim 15, further comprising estimating the error, wherein the error comprises a difference between the sequences of data as received by the receiving node, and the sequences of data as transmitted by the transmitting node” (Col.11, Lines 23-55; EN: This denotes that the error signal sent from the second layer neuron to the first layer neuron may be configured to be the absolute difference between the a teaching signal, which is comprised of a target signal that represents an expected result, and the layer output).
With regard to dependent claim 20,
Wierzynski fails to explicitly teach “The method of claim 18, wherein the sequences of data comprise sequences of data symbols selected from a predefined set of data symbols,” and “and wherein estimating the error comprises measuring a distance between a data symbol in a received sequence of data and a nearest data symbol in the predefined set of data symbols”.
Sinyavskiy teaches, “The method of claim 18, wherein the sequences of data comprise sequences of data symbols selected from a predefined set of data symbols,” (Col. 22, Lines 43-52; EN: This denotes that data can be encoded as a scalar vector). “and wherein estimating the error comprises measuring a distance between a data symbol in a received sequence of data and a nearest data symbol in the predefined set of data symbols” (Col.11, Lines 23-55 and Col. 22, Lines 43-52; EN: This denotes that the error signal sent from the second layer neuron to the first layer neuron may be configured to be the distance between the teaching signal, which can be seen as training input and is comprised of a target signal that represents an expected result, and the layer output. The neurons in this network can communicate using rate-based processes and data that can be encoded with scalar values and/or vector neuron output).
With regard to dependent claim 21,
The rest of this claim is similar in scope to claim 14 and is rejected under
a similar rationale.
With regard to independent claim 23,
The rest of this claim is similar in scope to claim 1 and is rejected under
a similar rationale.
With regard to independent claim 31,
The rest of this claim is similar in scope to claim 15 and is rejected under
a similar rationale.
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013 as applied to claim 1 above, and further in view of Zhang et al, NPL (TDSNN: From Deep Neural Networks to Deep Spike Neural Networks with Temporal-Coding), published July 17, 2019.
With regard to dependent claim 4,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. However, Wierzynski in view of Sinyavskiy fail to teach “The method of claim 1, wherein the encoding configuration comprises an inhibition time window during which the transmitting node is prohibited from transmitting one or more of the sequences of data”.
Zhang teaches “The method of claim 1, wherein the encoding configuration comprises an inhibition time window during which the transmitting node is prohibited from transmitting one or more of the sequences of data” (Pg.1321, Section: Theoretical Analysis, Subsection: Ticking Neuron Mechanism; EN: This denotes an inhibitory period where postsynaptic neurons cannot fire until all the presynaptic neurons have fired at least once. Once all the presynaptic neurons have fired, they will be inhibited until all the postsynaptic neurons have fired at least once).
Wierzynski, Sinyavskiy, and Zhang are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy with the conversion of DNN into a temporal-coding SNN utilizing LIF (leaky integrate-and fire)neurons of Zhang. One would be motivated to do so to improve the congestion modification within neural networks and the efficiency of spikes, temporal coding, and rate coding.
With regard to dependent claim 5,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. However, Wierzynski in view of Sinyavskiy fail to teach “The method of claim 4, wherein adapting the encoding configuration comprises adapting one or more of: a duration of the inhibition time window; and a periodicity of the inhibition time window”.
Zhang teaches “The method of claim 4, wherein adapting the encoding configuration comprises adapting one or more of: a duration of the inhibition time window; and a periodicity of the inhibition time window” (Pg.1321, Section: Theoretical Analysis, Subsection: Ticking Neuron Mechanism; EN: This denotes an inhibitory period where postsynaptic neurons cannot fire until all the presynaptic neurons have fired at least once. Once all the presynaptic neurons have fired, they will be inhibited until all the postsynaptic neurons have fired at least once. This inhibitory period depends on a time variable that must be large enough to cover the firing of all the presynaptic neurons or the postsynaptic neurons, or it could be the firing time of the neuron firing the last spike).
With regard to dependent claim 6,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. Wierzynski teaches, “The method of claim 4, wherein the indication of the encoding configuration comprises” (Paragraph 0067-0070; EN: This denotes that the neuron models will indicate whether a neuron has fired or not and the neuron that was fire will have the output spike information sent to a physical information unit (PHIT) router via a super neuron).
However, Wierzynski in view of Sinyavskiy fail to explicitly teach “an indication of the inhibition time window”.
Zhang teaches, “an indication of the inhibition time window” (Pg.1321, Section: Theoretical Analysis, Subsection: Ticking Neuron Mechanism; EN: This denotes an inhibitory period for postsynaptic neurons that starts when the process of converting the weights of a DNN to an SNN starts).
Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013 as applied to claim 1 above, and further in view of Zambrano et al, NPL (Sparse Computation in Adaptive Spiking Neural Networks), published January 7, 2019.
With regard to dependent claim 7,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. Wierzynski teaches, “The method of claim 1, wherein adapting the encoding configuration comprises:…” (Paragraph 0071-0073; EN: This denotes that the congestion controller can modify spike distribution based on a received indication of congestion, where the controller can increase or decrease the spike drop rate).
However, Wierzynski in view of Sinyavskiy fail to teach “…adapting a rate at which data is transmitted by the transmitting node, such that an absolute rate at which data is transmitted by the transmitting node is changed for two or more sequences of data,” and “but a relative rate between the two or more sequences of data is unchanged”.
Zambrano “…adapting a rate at which data is transmitted by the transmitting node, such that an absolute rate at which data is transmitted by the transmitting node is changed for two or more sequences of data, but a relative rate between the two or more sequences of data is unchanged” (: Pg.2-3, Fig. 1, Section: Introduction, Subsection: Adaptive Spike-based Coding; EN: This denotes an adaptive spike based coding scheme, which is a variant of rate coding that is measured by the effective sum of postsynaptic potentials (PSPs) on the target neuron. Due to the various aspects of this coding scheme, the dynamic threshold allows for the adaptation of the response from neurons).
Wierzynski, Sinyavskiy, and Zambrano are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy with the AdSNNs (Adaptive Spiking Neural Network) of Zambrano. One would be motivated to do so to improve the congestion modification within neural networks and make SNNs more adaptive to neural coding precision, while improving the efficiency of spikes, temporal encodings, and rate encodings.
With regard to dependent claim 8,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. However, Wierzynski in view of Sinyavskiy and Yu fail to explicitly teach “The method of claim 7, wherein adapting the rate at which data is transmitted by the transmitting node comprises: transcoding the sequences of data according to an adaptive tuning curve,” and “wherein the adaptive tuning curve adapts the average rate at which data is transmitted by the transmitting node”.
Zambrano teaches, “The method of claim 7, wherein adapting the rate at which data is transmitted by the transmitting node comprises: transcoding the sequences of data according to an adaptive tuning curve,” (Pg.2-3, Fig. 1, Section: Introduction, Subsection: Adaptive Spike-based Coding, Paragraph 3; EN: This denotes the use of an adaptive tuning curve). “wherein the adaptive tuning curve adapts the average rate at which data is transmitted by the transmitting node” (Pg.2-3, Fig. 1, Section: Introduction, Subsection: Adaptive Spike-based Coding, Paragraph 3; EN: This denotes that the adaptation parameters can change the firing rates for a given stimulus, which the adaptive tuning curve will adapt to).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013 as applied to claim 1 above, and further in view of Rubino et al, NPL (Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence), published November 13, 2020.
With regard to dependent claim 12,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. However, Wierzynski in view of Sinyavskiy fail to teach “The method according to claim 1, wherein adapting the encoding configuration comprises: adapting a maximum rate at which data can be transmitted by the transmitting node”.
Rubino teaches, “The method according to claim 1, wherein adapting the encoding configuration comprises: adapting a maximum rate at which data can be transmitted by the transmitting node” (Pg.51, Fig. 6-7, Section: 3, Subsection: B, paragraph 2-3; EN: This denotes that the maximum spiking frequency can increase or decrease depending on the duration of the refractory period).
Wierzynski, Sinyavskiy, and Rubino are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy with the mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes of Rubino. One would be motivated to do so to improve the congestion modification within neural networks, improve the efficiency of spikes, and optimize the design of the synapse integrator and of the adaptive neuron circuits.
Claims 13 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013 as applied to claims 1 and 15 above, and further in view of Yu et al, NPL (A brain-inspired spiking neural network model with temporal encoding and learning), published August 2, 2014 and Chowdhury et al, NPL (One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency), published October 1, 2021.
With regard to dependent claim 13,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 1. Wierzynski teaches “The method of claim 1, wherein adapting the encoding configuration based on the…” (Paragraph 0071-0073; EN: This denotes that the congestion controller can modify spike distribution based on a received indication of congestion, where the controller can increase or decrease the spike drop rate).
However, Wierzynski in view of Sinyavskiy fail to teach “feedback comprises applying a first encoding configuration when the error is a first level,”, “and applying a second encoding configuration when the error is a second level,”, “wherein the first level is greater than the second level,”, and “and wherein the first encoding configuration has a lower average data rate than the second encoding configuration”.
Yu teaches, “feedback comprises applying a first encoding configuration when the error is a first level,” (Pg. 6, Section 3, Paragraph 2-3; EN: This denotes two classes of errors based on whether a neuron fires when it isn’t supposed to or it doesn’t fire when it should and updating neuron weights). “and applying a second encoding configuration when the error is a second level,” (Pg. 6, Section 3, Paragraph 2-4; EN: This denotes two classes of errors based on whether a neuron fires when it isn’t supposed to or it doesn’t fire when it should. If a neuron fires two spikes when it shouldn’t and each error causes the neuron to update its weights, that can count as second encoding configuration and second error level). “wherein the first level is greater than the second level,” (Pg. 6, Section 3, Paragraph 2-4; EN: This denotes that the first or second error level can be greater than or less than another level depending on the coefficients within the dynamic updating method).
Wierzynski, Sinyavskiy, and Yu are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy with the spiking neural network utilizing leaky integrate-and-fire (LIF) neurons for pattern recognition of Yu. One would be motivated to do so to improve the congestion modification within neural networks and improve the efficiency of spikes, temporal coding, and rate coding.
However, Yu fails to explicitly teach “and wherein the first encoding configuration has a lower average data rate than the second encoding configuration”.
Chowdhury teaches, “and wherein the first encoding configuration has a lower average data rate than the second encoding configuration” (Pg.13, Section 5, Subsection: Reinforcement learning (RL) with IIR-SNN; EN: This denotes an increasing spike rate per layer with SNN-DQN (T1), which results in an average spike rate of 0.08. However, SNN-DQN (T5) has an average spike rate of 0.42).
Wierzynski, Sinyavskiy, Yu, and Chowdhury are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy, the spiking neural network utilizing leaky integrate-and-fire (LIF) neurons for pattern recognition of Yu with the Iterative Initialization and Retraining method for SNNs (IIR SNN) of Chowdhury. One would be motivated to do so to improve the congestion modification within neural networks and improve the efficiency of spikes, temporal coding, and rate coding.
With regard to dependent claim 17,
The rest of this claim is similar in scope to claim 13 and is rejected under
a similar rationale.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Wierzynski et al, U.S. PG PUB (US 20150112909 A1), filed on October 29, 2013 in view of Sinyavskiy et al, U.S. Patent (US 9489623 B1), filed on October 15, 2013 as applied to claims 15 and 18 above, and further in view of Wu et al, NPL (Training Spiking Neural Networks with Accumulated Spiking Flow), published May, 18, 2021.
With regard to dependent claim 19,
As discussed above, Wierzynski in view of Sinyavskiy teaches the method of claim 15 and 18. Wierzynski fails to explicitly teach “The method of claim 18, wherein the sequences of data comprise scalar values, and wherein estimating the error comprises” and “averaging the scalar values over a time period”.
Sinyavskiy teaches, “The method of claim 18, wherein the sequences of data comprise scalar values, and wherein estimating the error comprises” (Col.11, Lines 23-55 and Col. 22, Lines 43-52; EN: This denotes that the error signal sent from the second layer neuron to the first layer neuron may be configured to be the absolute difference between the teaching signal, which can be seen as training input and is comprised of a target signal that represents an expected result, and the layer output. The neurons in this network can communicate using rate-based processes and data that can be encoded with scalar values and/or vector neuron output).
However, Sinyavskiy fails to explicitly teach “averaging the scalar values over a time period”.
Wu teaches, “averaging the scalar values over a time period” (Pg.10322, Section: The SF-BP Method, Subsection: The Backward Process; EN: This denotes the use of accumulated spiking flow, which takes the accumulated inputs and outputs from spiking neurons over a whole-time window).
Wierzynski, Sinyavskiy, and Wu are considered to be analogous to the claimed invention due to the fact that they are all generally related to spiking neural networks. It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the congestion indication and modification of neural networks of Wierzynski, the parallel networks and measure of discrepancy within robotic controllers of Sinyavskiy with the backpropagation method for SNNs based on the accumulated spiking flow (ASF) of Wu. One would be motivated to do so to improve the congestion modification within neural networks, improve the SNN parameters, and improve the efficiency of spikes, temporal coding, and rate coding.
Conclusion
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/SETH CAPRIANO-UMARI SHELTON/Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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