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)(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, 7-9, 13-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Mody et al. (US 2020/0074287 A1), hereinafter “Mody”.
As per claim 1, Mody teaches an edge device (Figs. 1-2) configured to perform industrial control operations within a production environment that defines a physical location, the edge device comprising:
“a plurality of neural network layers that define a deep neural network” at [0035] and Fig. 4A;
(Mody teaches an autonomous processing module 212 (i.e., “the edge device”) comprise a neural network 400, which is utilized for classifying objects. The neural network 400 includes a plurality of neural network layers)
“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 [0030] and Figs. 1-2;
(Mody teaches the autonomous processing module 212 connected to the sensor fusion module 210 to perform autonomous processing needed for vehicle operation. The autonomous processing module obtains data from a plurality of sensors 106, 108)
“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 [0036] and Fig. 4C;
(Mody teaches the neural network operations may be computed by performing matrix operations on the input to simulate the neurons in the various layers. For example, a matrix multiplier 424 multiplies matrices A and B, the result of the multiplication is accumulated in an accumulator 426)
“train the deep neural network of the edge device to predict outputs of nonlinear matrix operations” at [0036];
(Mody teaches the neural network includes a nonlinear activation block 428 which is trained to perform nonlinear matrix operations and predict output matrix C)
“based on the training, generate an approximation of a nonlinear matrix operation on the data, the approximation defining the large scale matrix computation” at [0036].
(Mody teaches the nonlinear activation block 428 performs nonlinear matrix operations on the accumulated matrix and provides an output matrix C to the memory 422)
As per claim 2, Mody 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 [0035]-[0036].
As per claim 3, Mody 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 [0038]-[0042].
Claims 7-9, 13-14 recite similar limitations as in claims 1-3 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 4, 10 are rejected under 35 U.S.C. 103 as being unpatentable over Mody as applied to claims 1-3, 7-9, 13-14 above, and in view of Kwon et al. (US 2020/0364558 A1), hereinafter “Kwon”.
As per claims 4, 10, Mody teaches the device of claim 2 discussed above. Mody does not explicitly teach: “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” as claimed. However, Kwon teaches method for performing matrix operation using a neural network including the steps of “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 [0010]-[0016], [0058]-[0067]. Thus, it would have been obvious to one of ordinary skill in the art to combine Kwon with Mody’s teaching by utilizing the matrix decomposing method as suggested by Kwon to compress the matrix data so that “a compression ratio of a deep learning model may be improved without degrading the performance” and “when running a deep learning model utilizing the compressed deep learning model, power consumption may be reduced and delay in response time may be reduce”, as suggested by Kwon at [0030].
Claims 6, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mody 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, Mody 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 Mody’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 8/03/2026 have been fully considered but they are not persuasive. The examiner respectfully traverses Applicant’s arguments.
Regarding independent claims 1, 7 and 13, Applicant argued that Mody does not teach “train the deep neural network of the edge device to predict outputs of nonlinear matrix operation; and based on the training, generate an approximation of a nonlinear matrix operation on the data, the approximation defining the large scale matrix computation”. On the contrary, Mody at [0036] the neural network includes a nonlinear activation block 428 which is trained to predict an output matrix C (i.e., “an approximation of a nonlinear matrix operation”). The nonlinear activation block 428 operates on the accumulated matrix and provides an output matrix C to the memory 422.
Applicant further argued that “approximation is performed to replace the actual large scale matrix operation” and “Mody teaches that actual matrix operations are being performed, but not that these matrix operations are to be substituted by a deep neural network that performs approximate”. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “approximation is performed to replace the actual large scale matrix operation”, “matrix operations are to be substituted by a deep neural network that performs approximate”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Furthermore, as shown in Mody’s Fig. 4C reproduced below, the nonlinear activation (i.e., “a deep neural network”) does not perform actual matrix operation, but only operates on the accumulated matrix in block 426 to approximate /provide an output matrix C.
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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
August 27, 2026
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