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 .
The action is in response to communications filed on 05/31/2023. Claims 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 12/18/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted was filed on 12/18/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 8 is objected to because of the following informalities:
As per claim 8, the term “executable” in line 2 raises question as to whether the features are limiting, or merely refer to intended use.
Appropriate correction is required.
Claim Interpretation
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) is/are: “a set of processing units” in claim 15 and “at least one processing unit” in claims 15 and 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof (e.g. in paragraph 61).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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 § 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 1-2, 5-9, 12-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al. (US 20160328643 A1).
As per independent claim 1, Liu teaches a method comprising:
identifying a layer in a plurality of layers included in a neural network model, each layer in the plurality of layers comprising a plurality of weight values (e.g. in paragraph 46, “each layer has a plurality of nodes and each node has a weight matrix or “filter” that is used to filter or combine the data from the nodes of the previous layer”);
selecting a weight value from the plurality of weight values in the layer (e.g. in paragraph 46, “weight matrix of a particular node of a layer of the deep neural network”);
removing the weight value from the plurality of weight values in the layer to produce a modified version of the layer (e.g. in paragraph 47, “a sparse set of weights is retained in each filter… remove a large number (e.g., 90%) of non-zero weights in each filter (e.g., using thresholding or L1-norm minimization)… steps 704 and 706 can be iterated to gradually reduce the number of non-zero weights for each filter to achieve a sparse set of weights”); and
updating remaining weight values in the plurality of weight values in the modified version of the layer (e.g. in paragraph 47, “refine the remaining non-zero weights in each filter”),
wherein removing the weight value and updating the remaining weight values provides greater compression of the neural network model (e.g. in paragraph 47, “gradually reduce the number of non-zero weights for each filter to achieve a sparse set of weights”, i.e. greater compression) and reduces loss of accuracy of the neural network model (e.g. in paragraph 47, “an accuracy of the approximated deep neural network can be calculated using the training data after each iteration, and when the accuracy decreases by a certain amount, the method can be stopped and the approximated deep neural network resulting from the previous iteration can be used”, i.e. reduces loss of accuracy).
As per claim 2, the rejection of claim 1 is incorporated and Liu further teaches wherein the layer in the plurality of layers is a first layer in the plurality of layers, wherein selecting the weight value from the plurality of weight values is based on a plurality of outputs generated from a second layer in the plurality of layers, wherein the second layer in the plurality of layers is a previous adjacent layer with respect to the first layer (e.g. in paragraph 46, “each layer has a plurality of nodes and each node has a weight matrix or “filter” that is used to filter or combine the data from the nodes of the previous layer”).
As per claim 5, the rejection of claim 1 is incorporated and Liu further teaches wherein, updating the remaining weight values in the plurality of weight values in the modified version of the layer comprises modifying the remaining weight values in the plurality of weight values in a manner that minimizes an error between (1) the modified version of the layer and a plurality of outputs generated from a second layer in the plurality of layers and (2) an unmodified version of the layer and the plurality of outputs generated from the second layer in the plurality of layers (e.g. in paragraphs 46-47, “each layer has a plurality of nodes and each node has a weight matrix or “filter” that is used to filter or combine the data from the nodes of the previous layer… refine the remaining non-zero weights in each filter… an accuracy of the approximated deep neural network can be calculated using the training data after each iteration, and when the accuracy decreases by a certain amount, the method can be stopped and the approximated deep neural network resulting from the previous iteration can be used”, i.e. minimizes error).
As per claim 6, the rejection of claim 1 is incorporated and Liu further teaches repeatedly selecting a particular weight value from the plurality of weight values, removing the particular weight value from the plurality of weight values of the layer to produce a particular modified version of the layer, and, updating particular remaining weight values in the plurality of weight values in the particular modified version of the layer until a defined sparsity level is reached (e.g. in paragraph 47, “can be repeated for a large number of iterations to refine the non-zero weights to achieve greater accuracy… can be iterated to gradually reduce the number of non-zero weights for each filter to achieve a sparse set of weights… iterated until a target percentage of weights in each filter are set to zero”, i.e. sparsity level).
As per claim 7, the rejection of claim 1 is incorporated and Liu further teaches wherein the layer is a first layer, wherein the weight value is a first weight value (e.g. in paragraph 46, “each layer has a plurality of nodes and each node has a weight matrix or “filter” that is used to filter or combine the data from the nodes of the previous layer”), the method further comprising: identifying a second layer in the plurality of layers included in the neural network model (e.g. in paragraph 46, “each layer has a plurality of nodes and each node has a weight matrix or “filter” that is used to filter or combine the data from the nodes of the previous layer”); selecting a second weight value from the plurality of weight values in the second layer (e.g. in paragraph 46, “weight matrix of a particular node of a layer of the deep neural network”); removing the second weight value from the plurality of weight values in the second layer to produce a modified version of the second layer (e.g. in paragraph 47, “a sparse set of weights is retained in each filter… remove a large number (e.g., 90%) of non-zero weights in each filter (e.g., using thresholding or L1-norm minimization)… steps 704 and 706 can be iterated to gradually reduce the number of non-zero weights for each filter to achieve a sparse set of weights”); and updating remaining weight values in the plurality of weight values in the modified version of the second layer (e.g. in paragraph 47, “refine the remaining non-zero weights in each filter”).
Claims 8-9 and 12-14 are the medium claims corresponding to method claims 1-2 and 5-7 and are rejected under the same reasons set forth and Liu further teaches a non-transitory machine-readable medium storing a program executable by at least one processing unit of a device, the program comprising sets of instructions for performing the method (e.g. in paragraph 63).
Claims 15-16 and 19-20 are the system claims corresponding to method claims 1-2 and 5-6 and are rejected under the same reasons set forth and Liu further teaches a set of processing units and a non-transitory machine-readable medium storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to perform the method (e.g. in paragraph 63).
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 (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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 3-4, 10-11 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20160328643 A1) in view of Bouchard et al. (US 20200285956 A1).
As per claim 3, the rejection of claim 2 is incorporated, but Liu does not specifically teach wherein selecting the weight value in the plurality of weight values is based on a Hessian of the plurality of outputs generated from the second layer in the plurality of layers. However, Bouchard teaches selecting a weight value in a plurality of weight values is based on a Hessian of a plurality of outputs generated from a second layer in a plurality of layers (e.g. in paragraphs 70-71 and 102-103, “the Hessian matrix, which corresponds to the relation among the output layer weights”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Liu to include the teachings of Bouchard because one of ordinary skill in the art would have recognized the benefit of improving training.
As per claim 4, the rejection of claim 3 is incorporated and the combination further teaches wherein selecting the weight value in the plurality of weight values is further based on a gradient associated with the first layer (e.g. Liu, in paragraph 50, “back-propagation can be performed again using stochastic gradient descent (i.e., with the original cost function) to refine the remaining non-zero coefficients”; Bouchard, in paragraph 70, “gradient vector” and figure 2).
Claims 10-11 are the medium claims corresponding to method claims 3-4 and are rejected under the same reasons set forth.
Claims 17-18 are the system claims corresponding to method claims 3-4 and are rejected under the same reasons set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Deng et al. (US 20190180184 A1) teaches “provides a neural network that may include a plurality of layers in which each layer may include a set of weights w associated with the corresponding layer that enhance a speed performance of the neural network, an accuracy of the neural network, or a combination thereof… prune weights of a neural network” (e.g. in paragraphs 6-7).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144 05/01/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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