DETAILED ACTION
1. The present application 18/613,117, filed on 08/12/2026, is being examined under the first inventor to file provisions of the AIA . An election/restriction requirement was mailed on 06/26/2026. In response to the election/restriction requirement, Applicant elected the invention of claims 1-9 without traverse. Claims 10-18 have been canceled. Claims 1-9 are pending.
Drawings
2. The drawings received on 03/22/2024 are accepted by the Examiner.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 03/22/2024 and 08/02/2026 are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are a “main controller”, an “operation domain selector”, “an equivalent convertor” in claims 1-9.
If applicant does not intend to have these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
Claims 1-9 are rejected under 35 U.S.C. 101 because the claimed invention does not fall within one of the four statutory categories of invention. The claims recite functional software components, including a “main controller”, an “operation domain selector,” and an “equivalent converter,” without reciting a physical or tangible structural component. Accordingly, the claimed subject matter is directed to software per se and does not constitute a process, machine, manufacture or composition of matter.
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 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.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable by Ruckauer et al. (US 2019/0122110 A1), hereinafter Ruckauer and in view of Song et al. (US 2023/0042773 A1), hereinafter Song.
Referring to claim 1, Ruckauer discloses a low-power artificial intelligence (AI) processing system combining an artificial neural network (ANN) and a spiking neural network (SNN) (See para. [0044]-para. [0048], para. [0052]-para. [0058] and Figure 1, an SNN requires less processing power than an ANN employing MAC operations and provide higher power efficiency, note Figure 1 illustrates ANN 110 and SNN 120), the low-power AI processing system comprising:
an ANN including an artificial layer (See para. [0045], calculate respective activation values (or activations) of each of the neurons of each layer of the ANNs, the ANN 110 includes a plurality of layers, and each of the layers includes a plurality of neurons);
an SNN configured to output an artificial layer of the ANN as the same operation result (See para. [0049], para. [0059]-para. [0060] the pre-trained ANN is converted into an SNN and through the conversion, the SNN implements the trained objective of the ANN);
a main controller configured to calculate an ANN computational cost and an SNN computational cost […] See para. [0060], the computation cost of an SNN may increase as firing rate increases and compares the computational/energy characteristics of an SNN with an ANN processing, including SNN addition/memory-transfer operations and ANN MAC operations);
[…] comparing the ANN computational cost and the SNN computational cost (See para. [0060] and para. [0102], comparing the relative computation characteristics of SNN and ANN processing and explaining that an SNN may lose its computational advantage relative to the ANN as the firing rate increases); and
an equivalent converter configured to form a combined neural network by converting the artificial layer of the ANN into a spiking layer of the SNN according to selection of SNN operation domain (See para. [0059], para. [0060] and para. [0102], converting a pre-trained ANN into an SNN and determining a firing rate for each SNN node based on data of the corresponding pre-trained ANN).
Ruckauer does not explicitly teach calculating the neural-network computational cost for each artificial layer; and selecting an operation domain having the lower computation cost based on the comparison.
Song discloses calculating and comparing computational costs associated with neural-network operations and selecting a configuration having a lower computational cost for each artificial layer (See para. [0117]-para. [0120], para. [0142]-para. [0144] and para. [0117], calculating computation costs for the neural network operation, generating a plurality of schedule candidates and determining the schedule requiring the lowest computation cost by comparing the computation costs of the plurality of schedule candidates).
Therefore, it 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 was made to modify Ruckauer to calculate and compare the computation costs associated with the neural networks, as taught by Song. A skilled artisan would have been motivated to make such modification to reduce the computation cost associated with performing neural network operations (See Song, para. [0005). Such a modification would merely apply Song’s known computational cost comparison technique to Ruckauer’ s neural-network implementation to select the less computationally costly operation domain, with a reasonable expectation of success.
Claims 6-9 are rejected under 35 U.S.C. 103 as being unpatentable by Ruckauer (US 2019/0122110 A1) and in view of Song (US 2023/0042773 A1) and further in view of Panda (“Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks with backward Residual Connections, Stochastic Softmax and Hybridization”, 2020).
As to claim 6, Ruckauer in view of Song does not explicitly disclose calculates the ANN computational cost by calculating an artificial layer average computational energy Avg(E.sub.A).
Panda discloses calculates the ANN computational cost by calculating an artificial layer average computational energy Avg(E.sub.A) (See Section 7, Eq. (8), Eq. (10) and table 1, determining the computational cost/energy of an ANN convolutional layer according to Eq. (8): and determining Ann inference energy according to Eq. (10), the experiments measure energy or total compute cost for the ANN and compare it with its SNN counterpart)
Therefore, it 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 was made to modify Ruckauer, as modified by Song, to calculate the ANN computational cost based on computational energy, as taught by Panda, thereby enabling evaluation and improvement of neural network energy efficiency. Such a modification would merely apply Panda’s known technique to Ruckauer/Song’ s neural-network implementation with a reasonable expectation of success.
As to claim 7, Panda also discloses calculates the artificial layer average computational energy Avg(E.sub.A) using an equation Avg(E.sub.A)=(1−s.sub.A)(E.sub.mul+E.sub.add) that adds multiplicative computational energy E.sub.mul and additive computational energy E.sub.add to a value obtained by subtracting ANN sparsity S.sub.A from “1.” (See Section 7, Eq. (8), Eq. (10) and table 1, the ANN computation is performed using multiply-and-accumluate (MAC) operations, note table identifies Mult = 3.1 pJ, ADD = 0.1 pJ and MAC= 3.2pJ, thus EMAC=EMult + EADD).
As to claim 8, Ruckauer in view of Song does not explicitly disclose calculates the SNN computational cost by calculating spiking layer average computational energy Avg(E.sub.S).
Panda discloses calculates the SNN computational cost by calculating spiking layer average computational energy Avg(E.sub.S) (See Section 7, Eq. (9), Eq. (11) and table 1, calculating SNN computational cost/energy, FLOPSSNN = o2 x N x k2 x M x SA, where SA is the net spiking activity/number of firing neurons per layer, then ESNN = (
∈
F
LOPSSNN) x EAC x T).
Therefore, it 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 was made to modify Ruckauer, as modified by Song, to calculate the ANN computational cost based on computational energy, as taught by Panda, thereby enabling evaluation and improvement of neural network energy efficiency. Such a modification would merely apply Panda’s known technique to Ruckauer/Song’ s neural-network implementation with a reasonable expectation of success.
As to claim 9, Panda also discloses calculates the spiking layer average computational energy Avg(E.sub.S) using an equation Avg(E.sub.s) (1−s.sub.s)TE.sub.add that adds a number of time steps T and additive computation energy Eadd to a value obtained by subtracting SNN sparsity Ss from “1” (See Section 7, Eq. (9) and Eq. (11) and Table 1, FLOPSSNN = o2 x N x k2 x M x SA, where SA is the net spiking activity, i.e., the fraction/amount of neurons firing in the layer, then ESNN = (
∈
F
LOPSSNN) x EAC x T, where T is the number of SNN time steps and EAC is the energy of an accumulate/add operation).
Allowable Subject Matter
Claims 2-5 are objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Qin et al. (US 2022/00760095 A1) discloses a method for providing a neural network with multiple sparsity levels include sparsifying a matrix associated with the neural network to form a first sparse matrix; training the neural network using the first sparse matrix to form a second sparse matrix by fixing values and locations of non-zero elements of the first sparse matrix and updating a zero-value element of the first sparse matrix to be a non-zero value, wherein non-zero elements of the second sparse matrix includes the non-zero elements of the first sparse matrix; and outputting the second sparse matrix for executing the neural network.
Van et al. (US 2020/0143229 A1) discloses a method for an improved spiking neural network (SNN) configured to learn and perform unsupervised extraction of features from an input stream. An embodiment operates by receiving a set of spike bits corresponding to a set synapses associated with a spiking neuron circuit. The embodiment applies a first logical AND function to a first spike bit in the set of spike bits and a first synaptic weight of a first synapse in the set of synapses. The embodiment increments a membrane potential value associated with the spiking neuron circuit based on the applying. The embodiment determines that the membrane potential value associated with the spiking neuron circuit reached a learning threshold value. The embodiment then performs a Spike Time Dependent Plasticity (STDP) learning function based on the determination that the membrane potential value of the spiking neuron circuit reached the learning threshold value.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YUK TING CHOI whose telephone number is (571)270-1637. The examiner can normally be reached Monday-Friday 9am-6pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, AMY NG can be reached at 5712701698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YUK TING CHOI/Primary Examiner, Art Unit 2164
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