Notice of Pre-AIA or AIA Status
The present application 18/247,447, filed on 3/31/2023 (or after March 16, 2013), is being examined under the first inventor to file provisions of the AIA (First Inventor to File).
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 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.
This application is a 371 of PCT/US2022/053601 filed on 12/21/2022
PCT/US2022/053601 has PRO 63/386,959 filed on 12/12/2022
DETAILED ACTION
Response to Amendment
Claims 1,7,9,11,14-15 pending and claims, 2-6,8,10,12-13,16-20 are cancelled in this application.
Examiner acknowledges applicant’s amendment filed on 8/4/2026
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has been withdrawn pursuant to 37 CFR 1.114.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
3/26/2026 has been entered
Drawings
The Drawings filed on 3/14/2024 are acceptable for examination purpose.
Priority
Acknowledgment is made of applicant’s claim for domestic priority application
U.S. Provisional Patent application serial number # 63/386,959 filed on 12/12/2022
under 35 U.S.C. 119 (e)
Response to Arguments
Applicant's arguments filed 8/4/2026 with respect to claims 1,7,9,11,14-15 have been fully considered but they are not persuasive, for examiner’s response, see discussion below:
35 USC § 101
In view of applicant’s amendment, remarks, the rejection under 35 USC 101 as set forth in the previous office action is hereby withdrawn.
35 USC § 112
In view of applicant’s amendment, remarks, the rejection under 35 USC 112 as set forth in the previous office action is hereby withdrawn
Allowable Subject Matter
Claims 1,7,9,11,14-15 would be allowable if rewritten or amended or a terminal disclaimer filed to overcome the rejection(s) under nonstatutory double patenting, set forth in this Office Action
Double Patenting
At page 9-10, examiner acknowledges applicant’s remarks on double patent rejection, however, applicant may consider filing terminal disclaimer to overcome the double patent rejection, subject to approval
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1,7,9,11,14-15 (18/247,447 as amended 8/4/2026) are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of copending Application No. 17/770,049 (as filed on 6/25/2025-reference application). Although the claims at issue are not identical, they are not patentable distinct from each other because they are substantially similar in scope and they use the similar limitations to produce output relation diagram of plurality of attributes items.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented
instant application 18/247,447
co-pending Appl No 17/770,049 (as filed on 6/25/2025)
Claim 1,15. A non-transitory computer-readable medium including instructions executable by a processor to cause the processor to perform operations comprising:
partitioning a plurality of layers of a neural network model into a device partition and a server partition, wherein the neural network model consists of the device partition and the server partition;
transmitting, to a computation device, the device partition
performing a plurality of iterations of training, collaboratively with the computation device through a network, the neural network model, each iteration of training comprising
applying the server partition to a set of activations to obtain a set of output instances, the set of activations obtained by
reading, from an activation buffer, the set of activations as previously recorded, each activation output from the device partition upon application of the device partition to a training sample of the computation device,
applying a loss function relating activations to output instances to each output instance among the current set of output instances to obtain a set of loss values,
computing a set of gradient vectors for each layer of the server partition including a set of gradient vectors of a layer bordering the device partition, based on the set of loss values, and
updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition,
determining whether to transmit the set of gradient vectors of the layer bordering the device partition in response to at least one of confirming accordance with a predetermined schedule, a difference in gradient vectors from a previous iteration being greater than a threshold difference, or the set of loss values
exceeding a threshold loss, and
transmitting, to the computation device, the set of gradient vectors of the layer bordering the device partition in response to determining to transmit the set of gradient vectors of the layer bordering the device partition
wherein at least one iteration of training further comprise
comprises
receiving, as a single transmission from the computation device, the set of
activations in response to transmission of the set of activations from the
computation device
recording the set of activations to the activation buffer in response to receiving the set of activations;
wherein at least one iteration of training comprises reading the recorded set of activations without receiving the set of activations from the computation device;
receiving the device partition from the computation device; and
combining the device partition with the server partition to obtain an updated neural network model.
Claim 1, A computer-readable storage medium including instructions executable by a first server to cause the first server to perform operations comprising:
training, cooperatively with a computational device through a network, the neural network model by performing iterations of receiving, from the computational device, a feature map output from a device partition of a neural network model, the neural network model including a plurality of layers partitioned into the device partition and a server partition, applying the server partition to the feature map, updating gradient values and weight values of the layers of the server partition based on a loss function relating feature maps to output of the server partition, and transmitting, to the computational device, gradient values of a layer bordering the device partition and a loss value of the loss function, creating, during the iterations of training, a data checkpoint,
the data checkpoint including the gradient values and the weight values of the server partition, the loss value, and an optimizer state; receiving, during the iterations of training, a migration notice, the migration notice including an identifier of a second edge server; and transferring, during the iterations of training, the data checkpoint to the second edge server; wherein the second edge server receives the data checkpoint, and
resumes the iterations of training, cooperatively with the computational device through the network, the neural network model by utilizing the weight values of the server partition of the data checkpoint during the applying and the updating of a first iteration by the second edge server
It would have been obvious to a person of ordinary skill was made to modify and/or to omit the additional elements of claims 1,7,9,11,14-15 of the instant application 18/247,447 (as amended 8/4/2026) to arrive at the claims1-20 of U.S. copending application 17/770,049 (as filed on 6/25/2025) because the obvious limitation, the difference between computing a set of gradient vectors for each layer of the server partition including a set of gradient vectors of a layer bordering the device partition, based on the set of loss values, and updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition (instant application 18/247,447) and gradient values of a layer bordering the device partition and a loss value of the loss function, creating, during the iterations of training, a data checkpoint, (copending application 17/770,049 ( as filed on 6/25/2025) the ordinary skilled person would have realized that the remaining element(s) would perform the same function as before. Omission and/or addition of elements and its function in combination is obvious expedient if the remaining elements perform same functions as before
Claims 1,7,9,11,14-15 (18/247,447 as amended 8/4/2026) are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4,6-11,13-18,20 as amended 4/7/2026-of copending Application No. 18028765 -reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims perform the same steps as the claims in the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented
instant application 18/247,447
co-pending Appl No 18/028,765
Claim 1,15. A non-transitory computer-readable medium including instructions executable by a processor to cause the processor to perform operations comprising:
partitioning a plurality of layers of a neural network model into a device partition and a server partition, wherein the neural network model consists of the device partition and the server partition;
transmitting, to a computation device, the device partition
performing a plurality of iterations of training, collaboratively with the computation device through a network, the neural network model, each iteration of training comprising
applying the server partition to a set of activations to obtain a set of output instances, the set of activations obtained by
reading, from an activation buffer, the set of activations as previously recorded, each activation output from the device partition upon application of the device partition to a training sample of the computation device,
applying a loss function relating activations to output instances to each output instance among the current set of output instances to obtain a set of loss values,
computing a set of gradient vectors for each layer of the server partition including a set of gradient vectors of a layer bordering the device partition, based on the set of loss values, and
updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition,
determining whether to transmit the set of gradient vectors of the layer bordering the device partition in response to at least one of confirming accordance with a predetermined schedule, a difference in gradient vectors from a previous iteration being greater than a threshold difference, or the set of loss values
exceeding a threshold loss, and
transmitting, to the computation device, the set of gradient vectors of the layer bordering the device partition in response to determining to transmit the set of gradient vectors of the layer bordering the device partition
wherein at least one iteration of training further comprise
comprises
receiving, as a single transmission from the computation device, the set of
activations in response to transmission of the set of activations from the
computation device
recording the set of activations to the activation buffer in response to receiving the set of activations;
wherein at least one iteration of training comprises reading the recorded set of activations without receiving the set of activations from the computation device;
receiving the device partition from the computation device; and
combining the device partition with the server partition to obtain an updated neural network model.
A non-transitory computer-readable medium including instructions executable by a server to cause the server to perform operations comprising:
partitioning a plurality of layers of a neural network model into a device partition to be trained by a computation device and a server partition to be trained by the server
combining a plurality of encoding layers of an auto-encoder neural network with the device partition, wherein a largest encoding layer among the plurality of encoding layers is adjacent a layer of the device partition bordering the server partition;
combining a plurality of decoding layers of the auto-encoder neural network with the server partition, wherein a largest decoding layer among the plurality of decoding layers is adjacent a layer of the server partition bordering the device partition;
transmitting, to the computation device, the device partition combined with the plurality of encoding layers; and
training, collaboratively with the computation device through a network, the neural network model by receiving, from the computation device, after the computation device applies the device partition to a set of data samples to obtain a set of activations and applies the plurality of encoding layers to the set of activations to obtain a set of compressed activations, the set of compressed activations output from the plurality of encoding layers,
applying the plurality of decoding layers to the set of compressed activations to obtain a set of activations,
applying the server partition to the set of activations to obtain a set of output instances,
applying a loss function relating activations to output instances to each output instance among the current set of output instances to obtain a set of loss values,
computing a set of gradient vectors for each layer of the server partition, including a set of gradient vectors of a layer bordering the device partition, based on the set of loss
updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition, and
transmitting, to the computation device, the set of gradient vectors of the layer bordering the device partition,
wherein the computation device computes a set of gradient vectors for each layer of the device partition, based on the set of gradient vectors of the layer of the server partition bordering the device partition, and updates weight values of the device partition based on the set of gradient vectors for each layer of the device partition
It would have been obvious to a person of ordinary skill was made to modify and/or to omit the additional elements of claims 1,7,9,11,14-15 of the instant application 18/247,447 (as amended 8/4/2026)to arrive at the claims 1-4,7,6-11,13-18,20, of U.S. copending application 18/028,765 (as amended 4/7/2026) because the obvious limitation, the difference between a the difference between computing a set of gradient vectors for each layer of the server partition including a set of gradient vectors of a layer bordering the device partition, based on the set of loss values, and updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition (instant application 18/247,447) , while copending application 18/028,765 limitations applying a loss function relating activations to output instances to each output instance among the current set of output instances to obtain a set of loss values,
computing a set of gradient vectors for each layer of the server partition, including a set of gradient vectors of a layer bordering the device partition, based on the set of loss
updating weight values of the server partition based on the set of gradient vectors for each layer of the server partition, the ordinary skilled person would have realized that the remaining element(s) would perform the same function as before. Omission and/or addition of elements and its function in combination is obvious expedient if the remaining elements perform same functions as before
Conclusion
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax (not Examiner's Fax), Regular postal mail, or EFS Web using PTO/SB/439.
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 Srirama Channavajjala whose telephone number is 571-272-4108. The examiner can normally be reached on Monday-Friday from 8:00 AM to 5:30 PM Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gorney, Boris, can be reached on (571) 270- 5626. The fax phone numbers for the organization where the application or proceeding is assigned is 571-273-8300 Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free)
/Srirama Channavajjala/Primary Examiner, Art Unit 2154
Prosecution Insights