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 .
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 1, 3-5 and 8-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Khoram et al. (“Adaptive Quantization of Neural Networks” 2/23/2018, ICLR 2018) (hereinafter referred to as “Khoram”).
Re claims 1, and 9-10: Khoram teaches a method (and corresponding device and non-transitory computer program) of quantizing a deep neural network (DNN), previously trained during a training phase that determines a set of weights for each layer of said deep neural network, said method comprising:
a phase of quantizing said deep neural network, said phase comprising:
determining, for at least one layer of said each layer of said deep neural network, a disruption limit value of at least one weight of the set of weights of said at least one layer, beyond which an output of said deep neural network is erroneous (Section 1, pg. 2; “Introduction:… The output of the optimization problem is an error margin associated to each parameter: Section 2.1, pg. 2; “Problem Definition: …the solution of this minimization problem represents critical parameters with high precision to sustain high accuracy and assigns low precision to ineffectual ones or prunes them..”);
determining, for a target inference precision and from said disruption limit value, an adjustment limit value of said at least one weight of said set of weights (Section 1, pg. 2: “Introduction: …This margin is computed based on the loss function gradient if the parameter and is used to determine its precision…,: Section 2.1, “Problem definition:…We minimize the aggregate bit-widths of all network parameters while monitoring the training loss function. Due to the quantization noise, this function deviates from its optimum which was achieved through training. This noise effect can be controlled by introducing an upper bound on the loss function in order to maintain it reasonably close to the optimum…); and
decreasing an arithmetic precision of said at least one weight of said set of weights as a function of said adjustment limit value (Section 1, pg. 2: “Introduction: …This margin is computed based on the loss function gradient if the parameter and is used to determine its precision).
Re claim 3: Khoram teaches wherein the adjustment limit value is greater than the disruption limit value, and said determining said adjustment limit value comprises at least one iteration of operations, said operations comprising for each layer of the deep neural network, choosing a candidate adjustment value greater than said disruption limit value, modifying a value of said at least one weight of said each layer of said candidate adjustment value, and measuring an inference precision of said deep neural network thus modified on a test base; wherein said operations are reiterated until the candidate adjustment value is identified for which the inference precision that is measured corresponds to the target inference precision (Section 2: Proposed Quantization Algorithm).
Re claims 4-5: Khoram teaches wherein said decreasing said arithmetic precision comprises a zeroing of said at least one weight whose value is less than the adjustment limit value; wherein said decreasing said arithmetic precision comprises changing the arithmetic precision of said at least one weight to a less precise arithmetic precision (Section 1: Introduction).
Re claim 8: Khoram teaches wherein the deep neural network is trained for a classification of objects in at least two classes; or a regression of an item of input data in order to provide an item of output data (Section 3: Evaluation and Discussion).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2, and 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Khoram.
Re claim 2: Khoren does not explicitly teach wherein the adjustment limit value is equal to the disruption limit value.
However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention recognized that at best the adjustment limit value should be equal to the disruption limit value for the obvious reason of obtaining optimal result.
Re claims 6-7: Khoram does not explicitly teach wherein for said each layer, the disruption limit value is identified by a backward error technique applied to the set of weights of the deep neural network; wherein for said each layer, the disruption limit value is identified by a BERR statistical technique.
However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Khoram to include these features because the use of backward error techniques in DNN and the use of BERR statistics for value identification are conventional in the field.
Response to Arguments
Applicant's arguments filed 2/19/2026 have been fully considered but they are not persuasive.
Applicant argues that the concept taught by Khoram is different from that of the claimed invention. In particular, Applicant argues that “loss function” as taught by Khoram is used during the training of the neural network, whereas the “inference precision” in the claimed invention is used during the inference phase.
In response to applicant's argument, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, it does not matter what “phase” the invention is directed to, as long as it is reasonably pertinent to the particular problem with which the inventor was concerned. Applying similar concepts to different phases or other fields of endeavor does not materially distinguish over prior art.
Note: Claims 1-8 do not include any hardware components that could potentially raise 101 abstract idea issue (since this can be replicated using pen and paper). Amending the claims to recite “a computer-implemented method” will clarify this issue.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou et al (“Adaptive Quantization for Deep Neural Network”) ARXIV.org 12/4/2017.
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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/OLABODE AKINTOLA/Primary Examiner, Art Unit 3691