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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5, 7-11, 13-15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lau et al. (US 2019/0392297 A1), hereinafter “Lau”.
As per claim 1, Lau teaches an edge device configured to perform industrial control operations within a production environment that define a physical location, the edge device comprising:
“a plurality of neural network layers that define a deep neural network” at [0044]-[0052];
(Lau teaches network models such as multilayer perceptron (MLPs), restricted Boltzmann machines (RBMs), deep belief network (CNN) can be implemented using deep learning hardware (DLH) device)
“a processor; and a memory storing instructions that, when executed by the processor, cause the edge device to: obtain data from one or more sensors at the physical location defined by the production environment” at [0040]-[0041];
(Lau teaches obtaining sensor data from a plurality of sensor devices at the sources 110, 115)
“perform one or more matrix operations on the data using the plurality of neural network layers so as to generate a large scale matrix computation at the physical location defined by the production environment” at [0040]-[0052], [0074].
(Lau teaches a machine learning computing system 105 comprising a neural network (e.g., MLP, RBM/DBN, RNN, CNN), wherein the neural network is implemented using DLH devices and comprised the DLH devices. The machine learning computing system 105 accepts input data from sensor devices 110, and the neural network performs matrix operations on the input data using the DLH devices. Various algorithms and strategies may be used to scale network across multiple chips. When scaling a network across multiple nodes, both data parallelism and model parallelism may be employed. An example DLH device may be well adapted to accelerating distributed matrix multiplication. Various algorithms may be used to distribute matrix multiplication across multiple nodes)
As per claim 2, Lau teaches the device of claim 1, further cause the edge device to: perform a plurality of linear matrix operations on the data so as to generate the large scale matrix computation, each linear matrix operations performed on a respective layer of the plurality of neural network layers” at [0094]-[0099].
As per claim 3, Lau teaches the device of claim 2, further cause the edge device to “encoding an algorithm associated with the data into the plurality of linear matrix operation” at [0094]-[0099].
As per claim 4, Lau teaches the device of claim 2, further cause the edge device to “based on the data, decompose a matrix so as to define a matrix decomposition; and perform the one or more matrix operations on the matrix decomposition across multiple layers of the plurality of neural network layers” at [0101]-[0117].
As per claim 5, Lau teaches the device of claim 1, further cause the edge device to: “train the deep neural network of the edge device to predict output of nonlinear matrix operations; and based on the training, generate an approximation of a nonlinear matrix operation on the data, the approximation defining the large scale matrix computation” at [0044]-[0055], [0197]-[0212].
Claims 7-11, 13-15 recite similar limitations as in claims 1-5 and are therefore rejected by the same reasons.
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.
Claims 6, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Lau as applied to claims 1-5, 7-11, 13-15 above, and in view of Filippov et al. (US 2021/0404328 A1), hereinafter “Filippov”
As per claims 6, 12, Lau teaches the device of claim 1 discussed above. Filippov does not teach “send the large scale matrix computation to a digital twin simulation model associated with the production environment, so as to update the digital twin simulation model in real time” as claimed. However, Filippov teaches a method for implementing self-adapting digital twins includes the step of ““send the large scale matrix computation to a digital twin simulation model associated with the production environment, so as to update the digital twin simulation model in real time” at [0049]-[0056]. Thus, it would have been obvious to one of ordinary skill in the art to combine Filippov with Lau’s teaching in order to provide a digital twin model which can be updated in real-time, and therefore can be used to “minimize a process target function, such as the process time, total cost of the process”, as suggested by Filippov at [0010]-[0016].
Response to Arguments
Applicant's arguments filed 1/09/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding claim 1, Applicant argued that “Lau teaches that machine learning system may be utilized to implement products or service on various devices, including robots (see [0041], Lau). However, that does not anticipate that the DLH is configured to perform industrial control operations within a production environment”. However, the argued limitation is recited in the preamble and is simply a statement of intended use.
Preamble Statement Of Intended Use Has No Patentable Weight And Is Not Limiting. “A claim’s preamble may be limiting ‘if it recites essential structure or steps, or if it is ‘necessary to give life, meaning, and vitality’ to the claim.’ But, generally, ‘a preamble is not limiting ‘where a patentee defines a structurally complete invention in the claim body and uses the preamble only to state a purpose or intended use for the invention.’” Preamble language, in article of manufacture claim, “for permitting a user to write thereon without the use of a marking implement,” not a limitation because mere statement of intended use and not clearly relied upon in prosecution history to distinguish over prior art. “[T]hat a structural term in the preamble is part of the claim does not mean that the preamble’s statement of purpose or other description is also part of the claim. Marrin (Fed. Cir. 03/22/10) (claims anticipated); Outdry (Fed. Cir. 06/16/17) (aff’g PTAB obviousness decision; preamble (“A process for waterproofing leather”) “is simply a statement of intended use, not a separate claim limitation” and need not be disclosed in prior art reference); Acceleration Bay (Fed. Cir. 11/06/18) (aff’g that italicized language is intended use with no patentable weight: “A computer network for providing an information delivery service for a plurality of participants …”); Catalina (Fed. Cir. 05/08/02) (vacating Summ. J. non-infringement; italicized language in preamble not limiting: “system for controlling the selection and dispensing of product coupons at a plurality of remote terminals located at predesignated sites such as consumer stores wherein each terminal comprises…”); In re Rudy (Fed. Cir. 07/18/19) (non-precedential) (aff’g PTAB that preamble (“kit from which a web-mounting fishing plug can be assembled in a home environment”) is a mere intended use with no patentable weight).
Applicant further argued that Lau does not teach “perform one or more matrix operations on the data using the plurality of neural network layers”. On the contrary, Lau teaches at [0040], [0044]-[0052] a machine learning computing system 105 comprising a neural network (e.g., MLP, RBM/DBN, RNN, CNN), wherein the neural network is implemented using DLH devices and comprised the DLH devices. The machine learning computing system 105 accepts input data from sensor devices 110, and the neural network performs matrix operations on the input data using the DLH devices. The DLH devices are part of the neural network, therefore, the matrix operations are performed using the plurality of neural network layers, as required by the claims.
In light of the foregoing arguments, the 35 U.S.C 102 rejection is hereby sustained.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
February 2, 2026