DETAILED ACTION
This action is in response to claims filed 11 June 2026 for application 18238459 filed 26 August 2023. Currently claims 1, 3, 6-9 and 11 are pending.
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 § 102
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 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.
Claims 7-9 and 11 are rejected under 35 U.S.C. 102(A)(1) as being anticipated by Fang et al. (Encoding, model, and architecture: systematic optimization for spiking neural network in FPGAs).
Regarding claim 7, Fang discloses: A method for SNN learning and simulation, comprising following steps:
step S1: judging whether there is a need to read a network structure from a file, and if yes, directly performing step S5 to construct a network after the network structure is read; and if not, building the network structure anew, setting a network running time, and performing step S2 (Fig 6);
step S2: constructing an input layer, and adopting different processing manners for input data according to different input manners selected (§3.2 discloses parts of a SNN including input nodes for an input layer, Fig 6 SNN specification);
step S3: constructing a neuron layer in the form of neuron groups, defining a model used by each neuron group, a number of neurons comprised in the model, and specific model details, converting a differential equation of the neurons, and adding a difference equation obtained after conversion to a calculation graph (§3.2 discloses parts of a SNN including input nodes for an input layer, “Equation 18 to 23 provide an explicit way to update the state of SNN based on difference equations, hence it is convenient to implement in FPGAs.” P5§3.2 ¶3, eq18-23 disclose difference equations, “A computation graph is generated from the inference model. Each node indicates a stage in the layer-wise pipeline. Edge direction indicates data dependency. Node’s attributes include hyperparameter such as type, input/output size etc. of corresponding layer.” P7 §4.3 ¶2);
step S4: constructing a connection layer, selecting a synapse type, initializing connection weights at the same time, integrating information required to be passed to post-synaptic neurons after connection weight calculation, and adding the integrated information to the calculation graph (§3.2 discloses parts of a SNN including neurons and synapses, “Equation 18 to 23 provide an explicit way to update the state of SNN based on difference equations, hence it is convenient to implement in FPGAs.” P5§3.2 ¶3, eq18-23 disclose synapses and connections weights, A LIF neuron can be made by setting some parameters to 0, “A computation graph is generated from the inference model. Each node indicates a stage in the layer-wise pipeline. Edge direction indicates data dependency. Node’s attributes include hyperparameter such as type, input/output size etc. of corresponding layer.” P7 §4.3 ¶2);
step S5: constructing a network, assigning IDs representing categories and parent classes to each neuron group and connection in sequence, and then generating a specific calculation graph based on the IDs (“A computation graph is generated from the inference model. Each node indicates a stage in the layer-wise pipeline. Edge direction indicates data dependency. Node’s attributes include hyperparameter such as type, input/output size etc. of corresponding layer.” P7 §4.3 ¶2); and
step S6: simulating operation of the network, the neurons changing with time and settings of a neuron model, performing step-by-step calculation according to the calculation graph, and storing a trained network structure and parameters (“Each 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛 is defined as executing equation 18 to 23 once to update SNN states. 𝑇𝑖 is the cycles required by the 𝑖𝑡ℎ layer to update the for one step, and 𝐹𝑟𝑒𝑞 is the system clock frequency. At lower level, the resources in each module (i.e. synapse, matrix multiplication and convolution and thresholding) are shared by neurons in the same layer in a time multiplexed way.” P7 §4.2 ¶2).
in step S6, adding a monitor to monitor parameters of specified neurons, displaying the parameters visually, and storing displayed information (Fig 1 time encoding by LIF neuron).
Regarding claim 8, Fang discloses: The method for SNN learning and simulation of claim 7, wherein, when the input layer is constructed in step S2, if the input data is given spike data, using the input data is directly as spike input; if an encoding manner is given, encoding the input data according to different encoding manners and then inputting the input data; and otherwise, constructing different constant input currents or quadrature input currents as input according to the input data §3.2 discloses parts of a SNN including input nodes for an input layer, “In SNN, static data e.g. images have to be converted as spike trains in an encoding window 𝑇𝑒 for processing. Larger 𝑇𝑒 provides better precision, but at the cost of longer computation time. To evaluate the effectiveness of proposed coding method and SNN model, we studied the trade-off between𝑇𝑒 and accuracy.” P7 §5.1 ¶1).
Regarding claim 9, Fang discloses: The method for SNN learning and simulation of claim 7, wherein the synapse type in step S4 comprises a chemical synapse and an electrical synapse, and calculation formulas of different synapse types are added to the connection layer to construct connections (§3.2 eq 18-23 and the notation parameters that can be set to zero disclose different types of neurons and synapses such as those in a LIF neuron).
Regarding claim 11, Fang discloses: An apparatus for SNN learning and simulation, comprising a non-volatile memory, and one or more processors, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the method for SNN learning and simulation of claim 7 (p9 §6 various computer devices can be used to implement the SNN, “We tested the three networks on CPU, GPU, embedded GPU, and neuromorphic chip, and results are shown in Table 3” §5.3 ¶2).
Response to Arguments
Applicant’s arguments, see pp6-10, filed 11 June 2026, with respect to the rejections under 35 USC 102, 103 and 112b of claims 1-6 have been fully considered and are persuasive. The rejections under 35 USC 102, 103 and 112b of claims 1-6 have been withdrawn.
Applicant's arguments filed 11 June 2026 have been fully considered but they are not persuasive. The arguments presented do not directly apply to claim 7 as claim 7 does not recited setting a current and/or voltage nor a greyscale image. Fang discloses a monitor of a neuron in Figure 1. No specific argument has been presented for why this is not disclosed other than a statement that it is not disclosed.
Allowable Subject Matter
Claims 1, 3 and 6 are allowed.
Conclusion
THIS ACTION IS MADE FINAL. 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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/ERIC NILSSON/Primary Examiner, Art Unit 2151
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