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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/01/2026 has been entered.
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 define 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 5/01/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHANH B PHAM/Primary Examiner, Art Unit 2166
May 21, 2026
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