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 .
Election/Restrictions
Applicant’s election without traverse of Group III (Claims 17-20) in the reply filed on 07/24/2026 is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
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 17-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 17-20 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.
Claim 17 recites the limitation: “… determined according to a numerical model (emphasis added).” While the exact phrase “numerical model” appears verbatim in summary paragraphs [0027] and [0028], literal support alone does not satisfy the written description requirement. The specification demonstrates possession of only a narrow species (stochastic numerical optimization of neural network parameters). It lacks sufficient identifying characteristics or representative species to show that the inventor possessed the broader genus of revising parameters via any generic “numerical model”. In other words, the specification does not adequately disclose, describe or define what a “numerical model” is. The specification uses inconsistent phrasing for this exact step, describing it as: A “stochastic optimization model”, “numerical model”, “stochastic optimization” used to “numerically solve for the updates” via the Adam optimizer, or a “solution of a numerical optimization”. Because the specification conflates these concepts, a person having ordinary skill in the art cannot determine what specific computational mechanism qualifies as a “numerical model”. In machine learning, a “model” denotes a structural representation (such as a neural network or a Gaussian mixture distribution), whereas finding or updating parameters is an optimization algorithm or solver. Reciting that parameters are “determined according to a numerical model” confuses the data structure with the optimization method, rendering the boundaries of the step indefinite.
For the purposes of examination, the limitation is interpreted as the following:
“… determined according to a numerical optimization.”.
Claim Rejections - 35 USC § 102
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 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bonnet et al., hereinafter referred to as Bonnet (EP-4198825-A1).
As per claim 17, Bonnet discloses a non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations (Bonnet: Abstract.), the operations comprising:
identifying a number of mixture components of a mixture ensemble of an artificial neural network trained according to a set of features and a corresponding set of output values associated with the set of features (Bonnet: Para. [0005] discloses “BNN can be regarded as an ensemble of neural networks (NNs)”; Bonnet: Para. [0031] discloses “The BNN [claimed artificial neural network] is first trained on the basis of a training dataset denoted D = {x(i), z(i)} where x(i) is the input data [claimed set of features] on which the neural network has to be trained and z(i) (or z(i) if the output is scalar) the corresponding labels [claimed set of output values associated with the set of features]” and Bonnet: Para. [0036] discloses “the marginal posterior probability distribution of each synaptic coefficient can be approximated by a linear combination of a plurality K of Gaussian distributions [claimed identifying a number of mixture components], also referred to as Gaussian components [claimed mixture components of a mixture ensemble]”; (see also Bonnet: Para. [0053]));
determining a set of mixture weights and a set of mixture parameters of the mixture ensemble (Bonnet: Paras. [0036]-[0037] disclose “where g(.;µk, σk^2) denotes the kth Gaussian component, of mean µk and standard deviation σk [claimed set of mixture parameters], and where λk is a weighting factor [claimed set of mixture weights] … The set of parameters Ω = {λk, µk, σk | k = 1,...,K} of the GMM [claimed mixture ensemble] can be obtained [claimed determining] iteratively by using the expectation-maximization (EM) algorithm.”);
calculating a set of posterior probabilities according to the set of mixture weights and the set of mixture parameters (Bonnet: Para. [0039] discloses “the EM algorithm starts with an initial guess of the parameters ω(0) = {µk(0), σk(0) | k=1,...,K} [claimed set of mixture parameters] and estimates the membership probabilities of each sample, λk,j(0) [claimed calculating a set of posterior probabilities], that is the probability that sample wj originates from the kth Gaussian distribution [claimed according to the set of mixture weights and the set of mixture parameters].”); and
revising the set of mixture weights and the set of mixture parameters to obtain a revised set of mixture weights determined according to a sum of the set of posterior probabilities and a revised set of mixture parameters determined according to a numerical model (Bonnet: Para. [0039] discloses “the expectation of the log likelihood … is calculated over the samples w1, w2,..., wJ on the basis of their respective membership probabilities and set of parameters Ω(1) maximizing this expectation is derived [claimed revised set of mixture weights determined according to a sum of the set of posterior probabilities]. The parameters ω(1) = {µk(1), σk(1) | k=1,...,K} belonging to Ω(1) are then used as new estimates for a next step [claimed revising the set of mixture weights and the set of mixture parameters]” and Bonnet: Paras. [0040], [0053]-[0058] disclose “the maximization step of the likelihood (ML) of the mean values and standard deviations of the Gaussian components is performed under the constraint that (µ,σ) ∈ Γ [claimed revised set of mixture parameters determined according to a numerical model] … each pair (µ_kq, σ_kq) meets the constraint: µ_kq = ∑ ε_k,mq µ_k,mq, σ_kq = (∑ (h(µ_k,mq))^2)^0.5 where h is the hardware constraint linking the mean value and the standard deviation of the conductance [claimed numerical model]”.).
As per claim 18, Bonnet discloses the non-transitory, machine-readable medium of claim 17, wherein the operations further comprise:
repeating the determining, the calculating and the revising, to obtain a further revised set of mixture weights and a further revised set of mixture parameters (Bonnet: Para. [0039] discloses that “The parameters ω(1) = {µk(1),σk(1)|k=1,...,K} belonging to Ω(1) are then used as new estimates for a next step [claimed obtain a further revised set of mixture parameters]. The expectation calculation and maximization steps alternate until the expectation of logL(Ω;w) saturates [claimed repeating the determining, the calculating and the revising, to obtain a further revised set of mixture weights and a further revised set of mixture parameters]” and Bonnet: Para. [0087] discloses “The mean values and the parameters of the covariance matrices are then used as parameters for defining the new Gaussian components in the expectation step of the next iteration of the MDEM algorithm [claimed repeating the determining, the calculating and the revising, to obtain a further revised set of mixture weights and a further revised set of mixture parameters]”.).
As per claim 19, Bonnet discloses the non-transitory, machine-readable medium of claim 18, wherein the operations further comprise:
generating the mixture ensemble according to the further revised set of mixture weights and the further revised set of mixture parameters (Bonnet: Para. [0005] discloses “BNN can be regarded as an ensemble of neural networks (NNs) [claimed mixture ensemble] whose respective weights have been sampled from the posterior probability distribution”, Bonnet: Para. [0061] discloses “each synapse wq, q= 1,...,Q of the BNN is implemented by... repeating for each component k= 1,...,K, a pattern of M RRAM cells programmed by respectively injecting the programming currents [claimed generating the mixture ensemble according to [...]]”, Bonnet: Para. [0096] discloses “the mean values [claimed mixture parameters] and the weighting factors [claimed mixture weights] are transferred to the RRAM for the programming the memristors”, and Bonnet: Paras. [0097]-[0100] disclose “the BNN can be further trained on chip, in the present instance after they the RRAM cells have been programmed [claimed further revised] … the Q.K mean values and standard deviations can be updated … in order to reprogram it in a SET operation [claimed generating the mixture ensemble according to the further revised set of mixture weights and the further revised set of mixture parameters]”.).
As per claim 20, Bonnet discloses the non-transitory, machine-readable medium of claim 19, wherein the mixture ensemble further comprises a mixture of gaussian distributions determined according to the further revised set of mixture weights (Bonnet: Para. [0036] discloses “the marginal posterior probability distribution of each synaptic coefficient can be approximated by a linear combination of a plurality K of Gaussian distributions, also referred to as Gaussian components [claimed mixture ensemble further comprises a mixture of gaussian distributions] … where λ_k is a weighting factor [claimed mixture weights]” and Bonnet: Para. [0039] discloses “The parameters ω(1) = {μ_k(1),σ_k(1)|k=1,...,K} belonging to Ω(1) are then used as new estimates for a next step [claimed determined according to the further revised set of mixture weights]. The expectation calculation and maximization steps alternate until the expectation of logL(Ω;w) saturates”.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and can be viewed in the list of references.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PEET DHILLON whose telephone number is (571)270-5647. The examiner can normally be reached M-F: 5am-1:30pm.
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/PEET DHILLON/Primary Examiner
Art Unit: 2488
Date: 09-16-2026
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